Instantaneous network model diagnostics - unbalanced statistics
This file shows diagnostics for instantaneous network models fit using unbalanced racial/ethnic mixing matrices and degree terms as reported by egos. In this file, we fit a series of nested models by adding one term at a time to examine changes to model estimates, MCMC diagnostics, and network diagnostics.
Load packages and model fits
rm(list = ls())
suppressMessages(library("EpiModelHIV"))
library("latticeExtra")## Loading required package: lattice## Loading required package: RColorBrewerlibrary("knitr")
library("kableExtra")
load(file = "/homes/dpwhite/R/GitHub Repos/WHAMP/Model fits and simulations/Fit tests and debugging/est/fit.i.buildup.unbal.rda")Model terms and control settings
| Terms | Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 |
|---|---|---|---|---|---|---|---|---|
| edges | 478.5 | 478.5 | 478.5 | 478.5 | 478.5 | 478.5 | 478.5 | 478.5 |
| nodefactor.deg.main.deg.pers.0.1 | NA | NA | NA | 173.2 | 173.2 | 173.2 | 173.2 | 173.2 |
| nodefactor.deg.main.deg.pers.0.2 | NA | NA | NA | 37.2 | 37.2 | 37.2 | 37.2 | 37.2 |
| nodefactor.deg.main.deg.pers.1.0 | NA | NA | NA | 36.6 | 36.6 | 36.6 | 36.6 | 36.6 |
| nodefactor.deg.main.deg.pers.1.1 | NA | NA | NA | 135.4 | 135.4 | 135.4 | 135.4 | 135.4 |
| nodefactor.deg.main.deg.pers.1.2 | NA | NA | NA | 145.9 | 145.9 | 145.9 | 145.9 | 145.9 |
| nodefactor.riskg.O2 | NA | NA | NA | NA | NA | NA | 0.4 | 0.4 |
| nodefactor.riskg.O3 | NA | NA | NA | NA | NA | NA | 6.9 | 6.9 |
| nodefactor.riskg.O4 | NA | NA | NA | NA | NA | NA | 109.5 | 109.5 |
| nodefactor.riskg.Y1 | NA | NA | NA | NA | NA | NA | 1.3 | 1.3 |
| nodefactor.riskg.Y2 | NA | NA | NA | NA | NA | NA | 8.2 | 8.2 |
| nodefactor.riskg.Y3 | NA | NA | NA | NA | NA | NA | 70.8 | 70.8 |
| nodefactor.riskg.Y4 | NA | NA | NA | NA | NA | NA | 762.0 | 762.0 |
| nodefactor.race..wa.B | NA | 75.2 | 75.2 | 75.2 | 75.2 | 75.2 | 75.2 | 75.2 |
| nodefactor.race..wa.H | NA | 100.8 | 100.8 | 100.8 | 100.8 | 100.8 | 100.8 | 100.8 |
| nodefactor.region.EW | NA | NA | NA | NA | 83.4 | 83.4 | 83.4 | 83.4 |
| nodefactor.region.OW | NA | NA | NA | NA | 242.2 | 242.2 | 242.2 | 242.2 |
| nodematch.race..wa.B | NA | NA | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 | 2.5 |
| nodematch.race..wa.H | NA | NA | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 | 13.3 |
| nodematch.race..wa.O | NA | NA | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 | 286.9 |
| nodematch.region | NA | NA | NA | NA | NA | NA | NA | 382.8 |
| absdiff.sqrt.age | NA | NA | NA | NA | NA | 380.0 | 380.0 | 380.0 |
| nodematch.role.class.I | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
| nodematch.role.class.R | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf | -Inf |
The control settings for these models are:
set.control.ergm = control.ergm(MCMC.interval = 1e+5,
MCMC.samplesize = 7500,
MCMC.burnin = 1e+6,
MPLE.max.dyad.types = 1e+7,
MCMLE.maxit = 400,
parallel = np/2,
parallel.type="PSOCK"))MCMC diagnostics
Model 1
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## 0.3720 21.8648 0.1262 0.1276
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## -42.5122 -14.5122 0.4878 15.4878 43.4878
##
##
## Sample statistics cross-correlations:
## edges
## edges 1
##
## Sample statistics auto-correlation:
## Chain 1
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.016018241
## Lag 2e+05 0.004478845
## Lag 3e+05 -0.021908757
## Lag 4e+05 0.004070781
## Lag 5e+05 -0.014951207
## Chain 2
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.013984710
## Lag 2e+05 0.019331419
## Lag 3e+05 -0.030323245
## Lag 4e+05 0.043858126
## Lag 5e+05 -0.001033153
## Chain 3
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.003639058
## Lag 2e+05 0.004865561
## Lag 3e+05 -0.014513719
## Lag 4e+05 0.033034544
## Lag 5e+05 0.013242954
## Chain 4
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0113589055
## Lag 2e+05 0.0028440719
## Lag 3e+05 -0.0033111683
## Lag 4e+05 0.0003207483
## Lag 5e+05 0.0006422744
## Chain 5
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.008253553
## Lag 2e+05 0.012824488
## Lag 3e+05 0.022777469
## Lag 4e+05 -0.002275063
## Lag 5e+05 -0.013043688
## Chain 6
## edges
## Lag 0 1.000000000
## Lag 1e+05 0.009619496
## Lag 2e+05 0.040609295
## Lag 3e+05 0.031020583
## Lag 4e+05 0.006720460
## Lag 5e+05 -0.008003514
## Chain 7
## edges
## Lag 0 1.0000000000
## Lag 1e+05 -0.0143819029
## Lag 2e+05 -0.0001361824
## Lag 3e+05 -0.0026294537
## Lag 4e+05 0.0295354653
## Lag 5e+05 -0.0011827580
## Chain 8
## edges
## Lag 0 1.000000000
## Lag 1e+05 -0.023899635
## Lag 2e+05 0.002757309
## Lag 3e+05 0.014959656
## Lag 4e+05 -0.013742463
## Lag 5e+05 0.023638649
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.6633
##
## Individual P-values (lower = worse):
## edges
## 0.5071488
## Joint P-value (lower = worse): 0.5203464 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -0.971
##
## Individual P-values (lower = worse):
## edges
## 0.3315318
## Joint P-value (lower = worse): 0.3349909 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.8039
##
## Individual P-values (lower = worse):
## edges
## 0.4214728
## Joint P-value (lower = worse): 0.4318732 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.3837
##
## Individual P-values (lower = worse):
## edges
## 0.7011725
## Joint P-value (lower = worse): 0.7207989 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.379
##
## Individual P-values (lower = worse):
## edges
## 0.167823
## Joint P-value (lower = worse): 0.1682671 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 1.207
##
## Individual P-values (lower = worse):
## edges
## 0.2273946
## Joint P-value (lower = worse): 0.3840555 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## 0.1294
##
## Individual P-values (lower = worse):
## edges
## 0.8970132
## Joint P-value (lower = worse): 0.890929 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges
## -1.146
##
## Individual P-values (lower = worse):
## edges
## 0.2519562
## Joint P-value (lower = worse): 0.261183 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 2
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges 0.9614 21.88 0.12632 0.12731
## nodefactor.race..wa.B 0.2007 9.00 0.05196 0.05269
## nodefactor.race..wa.H 0.5336 10.59 0.06114 0.06185
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -41.51 -13.512 0.4878 15.488 44.49
## nodefactor.race..wa.B -17.19 -6.186 -0.1865 5.814 17.81
## nodefactor.race..wa.H -19.84 -6.835 0.1647 7.165 22.16
##
##
## Sample statistics cross-correlations:
## edges nodefactor.race..wa.B
## edges 1.0000000 0.39130141
## nodefactor.race..wa.B 0.3913014 1.00000000
## nodefactor.race..wa.H 0.4271832 0.09322341
## nodefactor.race..wa.H
## edges 0.42718321
## nodefactor.race..wa.B 0.09322341
## nodefactor.race..wa.H 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.020924539 -0.001728139 0.042471861
## Lag 2e+05 0.004657611 0.020599080 0.011588301
## Lag 3e+05 0.025309430 -0.001020049 0.003335766
## Lag 4e+05 0.001809844 0.012411804 0.002872256
## Lag 5e+05 0.010043244 0.009925257 -0.015583031
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.005990263 0.0324931877 -0.021898796
## Lag 2e+05 -0.004393225 -0.0108195034 -0.011794768
## Lag 3e+05 0.013725164 -0.0207867137 0.007152037
## Lag 4e+05 -0.019643857 -0.0004449611 -0.029858917
## Lag 5e+05 -0.003436412 0.0118199122 0.021592296
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.016831117 0.020028798 -0.02494689
## Lag 2e+05 0.006467388 -0.010391921 -0.01439160
## Lag 3e+05 0.018090986 0.036099575 -0.01374998
## Lag 4e+05 0.002675135 0.002341193 -0.01142790
## Lag 5e+05 0.043779205 -0.023044599 0.02792746
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000e+00 1.000000000
## Lag 1e+05 0.01188607 7.406158e-03 -0.008817549
## Lag 2e+05 0.01182311 1.970443e-02 0.044429840
## Lag 3e+05 -0.01168962 -1.790854e-03 -0.007306519
## Lag 4e+05 0.00638484 8.630448e-05 -0.001451798
## Lag 5e+05 0.01913623 2.178303e-02 -0.005134404
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.014777566 0.014141124 0.001062696
## Lag 2e+05 0.004846138 0.015698034 0.004022546
## Lag 3e+05 -0.002658842 -0.002430491 -0.014419914
## Lag 4e+05 0.015942513 0.022601804 0.009566082
## Lag 5e+05 -0.002040663 -0.011697905 0.004148877
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.0159125717 -0.015056973 0.0187918963
## Lag 2e+05 0.0165963665 0.013014869 -0.0161865146
## Lag 3e+05 0.0076073123 0.008702703 0.0059090585
## Lag 4e+05 0.0075842468 -0.004231786 0.0002864374
## Lag 5e+05 -0.0004512533 0.021194065 0.0045462134
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.016913346 -0.007530441 0.035930846
## Lag 2e+05 0.012270322 0.027091974 0.023234930
## Lag 3e+05 0.008185145 0.004328202 -0.006993389
## Lag 4e+05 -0.007786605 -0.040473450 -0.002667202
## Lag 5e+05 0.046634577 0.010396538 -0.006305833
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.022422046 -0.008454623 0.021156298
## Lag 2e+05 0.003685671 0.028369140 0.002074361
## Lag 3e+05 -0.000237236 -0.023406074 -0.003253354
## Lag 4e+05 -0.033875577 0.007127537 0.002089118
## Lag 5e+05 0.011989064 0.040213595 0.016461319
##
## Sample statistics burn-in diagnostic (Geweke):
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.346369 0.109260 0.004333
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1781834 0.9129959 0.9965425
## Joint P-value (lower = worse): 0.4138388 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 2.6311 3.6485 0.8415
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.008510509 0.000263736 0.400040999
## Joint P-value (lower = worse): 0.00154964 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.98471 0.08639 -1.07714
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3247675 0.9311566 0.2814191
## Joint P-value (lower = worse): 0.2950909 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6880 0.1541 0.2131
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4914245 0.8775562 0.8312774
## Joint P-value (lower = worse): 0.9048868 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8053 0.9113 0.2640
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4206751 0.3621394 0.7917578
## Joint P-value (lower = worse): 0.7717614 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0622 0.4474 1.4001
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2881595 0.6545878 0.1614761
## Joint P-value (lower = worse): 0.535964 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -2.1597 -0.2486 -1.6979
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.03079316 0.80368434 0.08953553
## Joint P-value (lower = worse): 0.1206014 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4026 -1.0203 0.2226
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.6872617 0.3075832 0.8238166
## Joint P-value (lower = worse): 0.6335743 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 3
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -5.697 21.723 0.12542 0.12560
## nodefactor.race..wa.B 5.746 9.038 0.05218 0.05261
## nodefactor.race..wa.H 5.884 11.052 0.06381 0.06369
## nodematch.race..wa.B -2.538 0.000 0.00000 0.00000
## nodematch.race..wa.H -5.777 2.737 0.01580 0.01575
## nodematch.race..wa.O 5.776 17.112 0.09880 0.09919
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -48.512 -20.5122 -5.512 8.488 37.488
## nodefactor.race..wa.B -11.186 -0.1865 5.814 11.814 23.814
## nodefactor.race..wa.H -14.835 -1.8353 6.165 13.165 28.165
## nodematch.race..wa.B -2.538 -2.5375 -2.538 -2.538 -2.538
## nodematch.race..wa.H -10.275 -7.2750 -6.275 -4.275 -0.275
## nodematch.race..wa.O -26.884 -5.8841 5.116 17.116 39.116
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges nodefactor.race..wa.B
## edges 1.0000000 0.412984215
## nodefactor.race..wa.B 0.4129842 1.000000000
## nodefactor.race..wa.H 0.4414689 -0.004569947
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.1226119 -0.002821983
## nodematch.race..wa.O 0.7858353 -0.001423039
## nodefactor.race..wa.H nodematch.race..wa.B
## edges 0.441468921 NA
## nodefactor.race..wa.B -0.004569947 NA
## nodefactor.race..wa.H 1.000000000 NA
## nodematch.race..wa.B NA 1
## nodematch.race..wa.H 0.496998811 NA
## nodematch.race..wa.O -0.003505582 NA
## nodematch.race..wa.H nodematch.race..wa.O
## edges 0.122611852 0.785835275
## nodefactor.race..wa.B -0.002821983 -0.001423039
## nodefactor.race..wa.H 0.496998811 -0.003505582
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 1.000000000 -0.003893702
## nodematch.race..wa.O -0.003893702 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0086966198 0.000235780 0.043097828
## Lag 2e+05 0.0004537199 -0.023071414 -0.024383476
## Lag 3e+05 0.0009233485 -0.007093079 -0.028156226
## Lag 4e+05 -0.0223691018 0.001701941 0.006689933
## Lag 5e+05 0.0378771843 -0.029648493 0.011797626
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.000652547 0.004782351
## Lag 2e+05 NaN -0.034080494 0.019086739
## Lag 3e+05 NaN -0.014957353 0.011821590
## Lag 4e+05 NaN -0.003393689 -0.020073926
## Lag 5e+05 NaN 0.005775433 0.016523175
## Chain 2
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.002572814 -0.027001837 -0.022026524
## Lag 2e+05 0.006107758 -0.002675363 -0.015853305
## Lag 3e+05 -0.017325991 -0.001056323 -0.003327586
## Lag 4e+05 -0.004377187 0.028144003 -0.016236346
## Lag 5e+05 0.013241851 -0.001157907 -0.009856191
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.006326331 0.009362979
## Lag 2e+05 NaN -0.001377208 0.003930569
## Lag 3e+05 NaN -0.026697610 -0.025889399
## Lag 4e+05 NaN -0.034348755 -0.006927948
## Lag 5e+05 NaN -0.018898894 0.008728606
## Chain 3
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01342858 -0.008169847 -0.014948317
## Lag 2e+05 -0.01866900 0.022765793 -0.035228080
## Lag 3e+05 0.01792369 0.049297066 0.005043247
## Lag 4e+05 -0.02086499 -0.014059883 -0.012144445
## Lag 5e+05 0.01625335 0.010508217 0.010895722
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.0000000000
## Lag 1e+05 NaN 0.006570180 -0.0072212197
## Lag 2e+05 NaN 0.019501574 -0.0073997329
## Lag 3e+05 NaN 0.006574234 0.0056657757
## Lag 4e+05 NaN -0.002996293 -0.0117000140
## Lag 5e+05 NaN 0.015145857 -0.0005154477
## Chain 4
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.005780135 0.005533543 -0.001029785
## Lag 2e+05 0.002000047 -0.008502600 -0.012204197
## Lag 3e+05 0.009920283 0.036455265 0.019654205
## Lag 4e+05 -0.014911219 0.012409217 -0.031390364
## Lag 5e+05 -0.021396208 0.005712549 -0.001318277
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN 0.0084489114 -0.003861306
## Lag 2e+05 NaN 0.0007888953 0.002553965
## Lag 3e+05 NaN -0.0061040750 0.002327480
## Lag 4e+05 NaN -0.0255807443 0.007355392
## Lag 5e+05 NaN 0.0321118075 -0.022250554
## Chain 5
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.018202872 0.0001218513 -0.005655794
## Lag 2e+05 -0.007556006 0.0277665833 0.021117064
## Lag 3e+05 -0.001006759 0.0241146429 -0.008363072
## Lag 4e+05 0.008570940 0.0121956376 0.015176457
## Lag 5e+05 0.006273073 -0.0178560047 -0.012413543
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.037590637 -0.007670929
## Lag 2e+05 NaN 0.008528925 -0.005019357
## Lag 3e+05 NaN 0.009187140 -0.006999959
## Lag 4e+05 NaN 0.009879336 -0.005015392
## Lag 5e+05 NaN 0.021124766 0.027970894
## Chain 6
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013507135 -0.010167929 -0.021319865
## Lag 2e+05 0.028370763 -0.004472883 0.018682094
## Lag 3e+05 0.001552200 -0.028760819 -0.019678276
## Lag 4e+05 -0.007619653 0.019090574 0.002325194
## Lag 5e+05 -0.011084018 -0.013195103 -0.009016884
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000e+00 1.0000000000
## Lag 1e+05 NaN -2.389309e-05 -0.0001420207
## Lag 2e+05 NaN -1.679521e-02 0.0212276394
## Lag 3e+05 NaN 1.507757e-02 0.0027330858
## Lag 4e+05 NaN 1.434195e-02 -0.0196526545
## Lag 5e+05 NaN 7.754742e-03 0.0073500373
## Chain 7
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0048343925 0.007318636 0.013655632
## Lag 2e+05 0.0405525089 0.002392997 0.038660708
## Lag 3e+05 -0.0079534448 -0.012064612 0.018916668
## Lag 4e+05 0.0004641492 -0.009410154 -0.026187897
## Lag 5e+05 -0.0094990065 0.009187710 0.007432344
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN 0.0294935696 -0.007903393
## Lag 2e+05 NaN 0.0296890374 0.039016007
## Lag 3e+05 NaN -0.0007543228 -0.002613443
## Lag 4e+05 NaN -0.0091877863 0.017140804
## Lag 5e+05 NaN 0.0062521557 -0.022640137
## Chain 8
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 -0.026535275 0.023963337 -0.0215778185
## Lag 2e+05 -0.013408855 0.002719366 -0.0004377148
## Lag 3e+05 0.018208741 0.001112258 0.0118847408
## Lag 4e+05 -0.001386932 0.002671070 0.0101711571
## Lag 5e+05 -0.014070527 -0.001869706 -0.0188477269
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000e+00
## Lag 1e+05 NaN 0.012877744 -1.545197e-05
## Lag 2e+05 NaN 0.007486491 1.240577e-02
## Lag 3e+05 NaN 0.003659438 -9.756031e-03
## Lag 4e+05 NaN -0.031324766 -6.141203e-03
## Lag 5e+05 NaN 0.031213351 2.973514e-03
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.6017 -0.8276 0.4741
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.4902 -0.4053
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5473409 0.4079075 0.6354477
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.1361719 0.6852263
## Joint P-value (lower = worse): 0.6448022 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8193 0.3159 0.4620
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.6071 0.7559
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4126217 0.7520466 0.6440820
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.5437584 0.4497089
## Joint P-value (lower = worse): 0.9656356 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3489 0.1086 -1.7112
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.4651 1.5527
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.72716135 0.91351094 0.08703814
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.64185140 0.12049224
## Joint P-value (lower = worse): 0.1714478 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.7913 0.2382 -1.1382
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.2727 -0.2143
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4287827 0.8117570 0.2550202
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.2031182 0.8303179
## Joint P-value (lower = worse): 0.3518503 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.07438 -0.98794 -0.41489
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.43932 0.94797
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.9407053 0.3231832 0.6782210
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.6604280 0.3431451
## Joint P-value (lower = worse): 0.7327236 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -1.0925 -1.4804 0.6393
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -1.6687 -1.2655
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.27461738 0.13877829 0.52261733
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.09516917 0.20568379
## Joint P-value (lower = worse): 0.1229187 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## -0.9656 0.2359 0.5926
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN -0.5475 -1.8293
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.33425109 0.81353292 0.55343486
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.58406565 0.06736037
## Joint P-value (lower = worse): 0.4728179 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 2.0542 0.7466 1.2969
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 1.0037 1.4942
##
## Individual P-values (lower = worse):
## edges nodefactor.race..wa.B nodefactor.race..wa.H
## 0.03995882 0.45532357 0.19465634
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## NaN 0.31551480 0.13511693
## Joint P-value (lower = worse): 0.5117922 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 4
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -5.88693 21.646 0.12497 0.12558
## nodefactor.deg.main.deg.pers.0.1 0.06243 14.296 0.08254 0.08222
## nodefactor.deg.main.deg.pers.0.2 0.12793 6.236 0.03600 0.03616
## nodefactor.deg.main.deg.pers.1.0 -0.04241 6.134 0.03541 0.03659
## nodefactor.deg.main.deg.pers.1.1 -0.04455 12.434 0.07179 0.07319
## nodefactor.deg.main.deg.pers.1.2 -0.13277 12.979 0.07494 0.07487
## nodefactor.race..wa.B 5.92377 8.983 0.05186 0.05190
## nodefactor.race..wa.H 5.37685 10.990 0.06345 0.06306
## nodematch.race..wa.B -2.53754 0.000 0.00000 0.00000
## nodematch.race..wa.H -5.83086 2.739 0.01582 0.01580
## nodematch.race..wa.O 5.86285 16.989 0.09808 0.09848
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -47.512 -20.5122 -6.5122 8.488 36.488
## nodefactor.deg.main.deg.pers.0.1 -27.175 -10.1754 -0.1754 9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217 -4.2169 -0.2169 3.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568 -4.5677 -0.5677 4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364 -8.3643 -0.3643 8.636 24.636
## nodefactor.deg.main.deg.pers.1.2 -24.870 -8.8703 0.1297 8.130 26.130
## nodefactor.race..wa.B -11.186 -0.1865 5.8135 11.814 23.814
## nodefactor.race..wa.H -15.835 -1.8353 5.1647 13.165 27.165
## nodematch.race..wa.B -2.538 -2.5375 -2.5375 -2.538 -2.538
## nodematch.race..wa.H -10.275 -7.2750 -6.2750 -4.275 -0.275
## nodematch.race..wa.O -26.884 -5.8841 6.1159 17.116 40.116
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5601332
## nodefactor.deg.main.deg.pers.0.2 0.2714288
## nodefactor.deg.main.deg.pers.1.0 0.2759422
## nodefactor.deg.main.deg.pers.1.1 0.5028595
## nodefactor.deg.main.deg.pers.1.2 0.5091837
## nodefactor.race..wa.B 0.4171243
## nodefactor.race..wa.H 0.4418825
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.1194725
## nodematch.race..wa.O 0.7870153
## nodefactor.deg.main.deg.pers.0.1
## edges 0.56013323
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07137914
## nodefactor.deg.main.deg.pers.1.0 0.08425267
## nodefactor.deg.main.deg.pers.1.1 0.14271804
## nodefactor.deg.main.deg.pers.1.2 0.13628091
## nodefactor.race..wa.B 0.25599109
## nodefactor.race..wa.H 0.23286265
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.06479252
## nodematch.race..wa.O 0.43815248
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27142882
## nodefactor.deg.main.deg.pers.0.1 0.07137914
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03479577
## nodefactor.deg.main.deg.pers.1.1 0.07896060
## nodefactor.deg.main.deg.pers.1.2 0.06519433
## nodefactor.race..wa.B 0.12338815
## nodefactor.race..wa.H 0.11397616
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03222544
## nodematch.race..wa.O 0.21206716
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27594216
## nodefactor.deg.main.deg.pers.0.1 0.08425267
## nodefactor.deg.main.deg.pers.0.2 0.03479577
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.07251766
## nodefactor.deg.main.deg.pers.1.2 0.06895669
## nodefactor.race..wa.B 0.10328107
## nodefactor.race..wa.H 0.12863974
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03636133
## nodematch.race..wa.O 0.21963029
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50285950
## nodefactor.deg.main.deg.pers.0.1 0.14271804
## nodefactor.deg.main.deg.pers.0.2 0.07896060
## nodefactor.deg.main.deg.pers.1.0 0.07251766
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13174709
## nodefactor.race..wa.B 0.17847020
## nodefactor.race..wa.H 0.21371241
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.05174888
## nodematch.race..wa.O 0.41645037
## nodefactor.deg.main.deg.pers.1.2
## edges 0.50918368
## nodefactor.deg.main.deg.pers.0.1 0.13628091
## nodefactor.deg.main.deg.pers.0.2 0.06519433
## nodefactor.deg.main.deg.pers.1.0 0.06895669
## nodefactor.deg.main.deg.pers.1.1 0.13174709
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.18916310
## nodefactor.race..wa.H 0.26356744
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.07792721
## nodematch.race..wa.O 0.39082433
## nodefactor.race..wa.B
## edges 0.417124271
## nodefactor.deg.main.deg.pers.0.1 0.255991085
## nodefactor.deg.main.deg.pers.0.2 0.123388148
## nodefactor.deg.main.deg.pers.1.0 0.103281068
## nodefactor.deg.main.deg.pers.1.1 0.178470198
## nodefactor.deg.main.deg.pers.1.2 0.189163103
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H -0.003288847
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.002747197
## nodematch.race..wa.O 0.005310808
## nodefactor.race..wa.H
## edges 0.441882467
## nodefactor.deg.main.deg.pers.0.1 0.232862649
## nodefactor.deg.main.deg.pers.0.2 0.113976163
## nodefactor.deg.main.deg.pers.1.0 0.128639738
## nodefactor.deg.main.deg.pers.1.1 0.213712413
## nodefactor.deg.main.deg.pers.1.2 0.263567440
## nodefactor.race..wa.B -0.003288847
## nodefactor.race..wa.H 1.000000000
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.495733897
## nodematch.race..wa.O -0.002201865
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.119472524
## nodefactor.deg.main.deg.pers.0.1 NA 0.064792520
## nodefactor.deg.main.deg.pers.0.2 NA 0.032225440
## nodefactor.deg.main.deg.pers.1.0 NA 0.036361333
## nodefactor.deg.main.deg.pers.1.1 NA 0.051748878
## nodefactor.deg.main.deg.pers.1.2 NA 0.077927209
## nodefactor.race..wa.B NA 0.002747197
## nodefactor.race..wa.H NA 0.495733897
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA -0.008669426
## nodematch.race..wa.O
## edges 0.787015294
## nodefactor.deg.main.deg.pers.0.1 0.438152482
## nodefactor.deg.main.deg.pers.0.2 0.212067162
## nodefactor.deg.main.deg.pers.1.0 0.219630291
## nodefactor.deg.main.deg.pers.1.1 0.416450370
## nodefactor.deg.main.deg.pers.1.2 0.390824329
## nodefactor.race..wa.B 0.005310808
## nodefactor.race..wa.H -0.002201865
## nodematch.race..wa.B NA
## nodematch.race..wa.H -0.008669426
## nodematch.race..wa.O 1.000000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.003263543 -0.031492481
## Lag 2e+05 -0.000960946 0.006988825
## Lag 3e+05 0.002966553 0.009003673
## Lag 4e+05 -0.006399652 -0.015414791
## Lag 5e+05 -0.008084676 -0.014306557
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0086773540
## Lag 2e+05 -0.0002903341
## Lag 3e+05 -0.0234441222
## Lag 4e+05 0.0192747318
## Lag 5e+05 -0.0270200735
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.00000000
## Lag 1e+05 -0.01718846
## Lag 2e+05 -0.01608196
## Lag 3e+05 0.04066358
## Lag 4e+05 0.02077820
## Lag 5e+05 -0.01188138
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.031198865
## Lag 2e+05 0.012334951
## Lag 3e+05 0.001374661
## Lag 4e+05 -0.014159130
## Lag 5e+05 0.036058866
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.008174722 -0.005805584
## Lag 2e+05 -0.003893731 -0.036307056
## Lag 3e+05 -0.009316215 -0.007567145
## Lag 4e+05 0.010438611 0.004178925
## Lag 5e+05 -0.011314959 0.025905974
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.003828615 NaN -0.012570417
## Lag 2e+05 -0.008867698 NaN 0.025563282
## Lag 3e+05 -0.005731050 NaN 0.013429452
## Lag 4e+05 0.002027726 NaN -0.004304579
## Lag 5e+05 0.023477873 NaN 0.022949063
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.014659939
## Lag 2e+05 0.010149369
## Lag 3e+05 -0.009267030
## Lag 4e+05 -0.005294072
## Lag 5e+05 0.002193857
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.035406129 0.005749568
## Lag 2e+05 -0.021357683 -0.007223589
## Lag 3e+05 -0.014073849 -0.003216375
## Lag 4e+05 0.005116832 -0.002250077
## Lag 5e+05 -0.011261133 -0.017385621
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.008712529
## Lag 2e+05 -0.017318064
## Lag 3e+05 -0.000732260
## Lag 4e+05 -0.012515749
## Lag 5e+05 -0.007133813
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.007060592
## Lag 2e+05 -0.016195632
## Lag 3e+05 0.017268577
## Lag 4e+05 0.032368429
## Lag 5e+05 0.007394705
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.002834630
## Lag 2e+05 -0.001692535
## Lag 3e+05 0.009794797
## Lag 4e+05 0.005026994
## Lag 5e+05 -0.044407513
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.004223682 -0.005515692
## Lag 2e+05 -0.001781449 -0.018841418
## Lag 3e+05 0.003559444 0.001295144
## Lag 4e+05 -0.001975882 -0.006754649
## Lag 5e+05 0.035465141 -0.021011004
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.009749477 NaN -0.03042835
## Lag 2e+05 0.014043630 NaN -0.01613967
## Lag 3e+05 -0.012732948 NaN -0.00534948
## Lag 4e+05 0.001109932 NaN 0.01480938
## Lag 5e+05 -0.025688376 NaN 0.01670770
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.008825420
## Lag 2e+05 -0.025938454
## Lag 3e+05 -0.018607369
## Lag 4e+05 0.001104774
## Lag 5e+05 0.030904239
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 -0.0047470592 -0.018440412
## Lag 2e+05 0.0137951265 -0.013405216
## Lag 3e+05 0.0234161237 0.020375913
## Lag 4e+05 0.0009626197 0.010562116
## Lag 5e+05 -0.0261694584 0.008936712
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0183477892
## Lag 2e+05 -0.0169897817
## Lag 3e+05 -0.0059682717
## Lag 4e+05 -0.0002367892
## Lag 5e+05 0.0047053843
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000e+00
## Lag 1e+05 -4.146255e-03
## Lag 2e+05 6.622337e-03
## Lag 3e+05 -2.004038e-02
## Lag 4e+05 1.127230e-02
## Lag 5e+05 -6.245828e-05
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.010231833
## Lag 2e+05 0.028505444
## Lag 3e+05 0.033256563
## Lag 4e+05 0.008604250
## Lag 5e+05 -0.005237767
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.022776394 0.0081392672
## Lag 2e+05 0.008177325 -0.0001069287
## Lag 3e+05 0.004371953 -0.0150352332
## Lag 4e+05 0.023281646 0.0120224838
## Lag 5e+05 -0.023434770 -0.0306500886
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.010104469 NaN 0.003659839
## Lag 2e+05 -0.021140203 NaN 0.003565070
## Lag 3e+05 0.020465451 NaN -0.001725216
## Lag 4e+05 0.018253931 NaN 0.019376256
## Lag 5e+05 0.007592792 NaN -0.026398520
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.004878704
## Lag 2e+05 0.006366966
## Lag 3e+05 0.036764629
## Lag 4e+05 0.002636258
## Lag 5e+05 -0.018327430
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.002531248 0.006319107
## Lag 2e+05 -0.011796605 -0.002066789
## Lag 3e+05 0.014840811 0.002803799
## Lag 4e+05 -0.010317062 -0.008207676
## Lag 5e+05 0.001520677 0.007172441
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.018654250
## Lag 2e+05 -0.001484131
## Lag 3e+05 -0.009058657
## Lag 4e+05 0.003517378
## Lag 5e+05 -0.008616816
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.006219228
## Lag 2e+05 -0.011678744
## Lag 3e+05 -0.008052535
## Lag 4e+05 -0.039018503
## Lag 5e+05 0.023993069
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.003857160
## Lag 2e+05 -0.009434225
## Lag 3e+05 -0.030670121
## Lag 4e+05 0.004850767
## Lag 5e+05 -0.030521676
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 -0.003898732 -0.02718692
## Lag 2e+05 -0.019877398 -0.01133573
## Lag 3e+05 0.024823076 0.01992790
## Lag 4e+05 -0.007008696 0.01394109
## Lag 5e+05 0.020755595 -0.00669053
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.001437371 NaN 0.004244873
## Lag 2e+05 0.009043652 NaN -0.011313711
## Lag 3e+05 0.004892896 NaN 0.012552805
## Lag 4e+05 -0.009552151 NaN -0.001030845
## Lag 5e+05 -0.009461078 NaN 0.015668314
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0063793300
## Lag 2e+05 -0.0001685022
## Lag 3e+05 0.0089177684
## Lag 4e+05 -0.0212850838
## Lag 5e+05 -0.0116800132
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.016192713 0.012401134
## Lag 2e+05 -0.012760646 -0.004856308
## Lag 3e+05 0.001878554 -0.011987955
## Lag 4e+05 -0.016701194 0.028152006
## Lag 5e+05 -0.006181976 0.025153672
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.004678573
## Lag 2e+05 0.007100388
## Lag 3e+05 0.024197972
## Lag 4e+05 -0.001169242
## Lag 5e+05 -0.014810261
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.002217407
## Lag 2e+05 0.013080852
## Lag 3e+05 -0.024632425
## Lag 4e+05 0.012929141
## Lag 5e+05 0.014592882
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.001952058
## Lag 2e+05 0.028095377
## Lag 3e+05 0.015104246
## Lag 4e+05 -0.006862831
## Lag 5e+05 -0.034001919
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.003188849 -0.001503055
## Lag 2e+05 -0.007436318 -0.005577744
## Lag 3e+05 0.017910098 0.006289457
## Lag 4e+05 0.018260151 -0.022181114
## Lag 5e+05 0.005441830 0.006788565
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.006685837 NaN -0.018180982
## Lag 2e+05 -0.002697884 NaN -0.009766456
## Lag 3e+05 0.003965997 NaN 0.018889661
## Lag 4e+05 0.005260389 NaN -0.005538738
## Lag 5e+05 -0.011365350 NaN -0.015179972
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.022150511
## Lag 2e+05 -0.004910913
## Lag 3e+05 0.017863772
## Lag 4e+05 0.013487372
## Lag 5e+05 -0.022911707
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.008806552 0.0001296644
## Lag 2e+05 -0.019364598 0.0027380583
## Lag 3e+05 -0.001772476 -0.0126630530
## Lag 4e+05 0.008720581 0.0127149116
## Lag 5e+05 -0.002764916 0.0058995020
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.030186936
## Lag 2e+05 0.016829321
## Lag 3e+05 -0.001571619
## Lag 4e+05 0.005095037
## Lag 5e+05 -0.016096941
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.009736809
## Lag 2e+05 -0.005264595
## Lag 3e+05 0.013386446
## Lag 4e+05 -0.004239663
## Lag 5e+05 0.007265640
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.018691525
## Lag 2e+05 0.019440747
## Lag 3e+05 -0.003357697
## Lag 4e+05 0.011739150
## Lag 5e+05 -0.008798493
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.012550016 -0.008745848
## Lag 2e+05 -0.004722481 -0.008298144
## Lag 3e+05 -0.011960508 -0.011083076
## Lag 4e+05 -0.001004909 0.003207517
## Lag 5e+05 -0.028039003 0.011111779
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.052827700 NaN -0.018444288
## Lag 2e+05 -0.011920154 NaN -0.005288119
## Lag 3e+05 -0.011513453 NaN -0.027819924
## Lag 4e+05 0.005540105 NaN 0.034080297
## Lag 5e+05 0.004567508 NaN -0.020029481
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 -0.016781040
## Lag 2e+05 -0.017097375
## Lag 3e+05 0.008884166
## Lag 4e+05 0.025308595
## Lag 5e+05 0.007439243
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.004026595 0.0135524609
## Lag 2e+05 0.006444475 0.0005624462
## Lag 3e+05 0.013511247 -0.0026964830
## Lag 4e+05 0.001043332 -0.0066436201
## Lag 5e+05 -0.016615236 -0.0105483732
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.004218392
## Lag 2e+05 -0.001823677
## Lag 3e+05 0.012882662
## Lag 4e+05 -0.023948954
## Lag 5e+05 -0.021665809
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.010355376
## Lag 2e+05 0.005911978
## Lag 3e+05 0.002884730
## Lag 4e+05 0.005893478
## Lag 5e+05 -0.013912435
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0151243691
## Lag 2e+05 0.0002728808
## Lag 3e+05 -0.0200123451
## Lag 4e+05 -0.0026173031
## Lag 5e+05 0.0088408013
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.010726183 0.0155439543
## Lag 2e+05 0.006653098 0.0115415540
## Lag 3e+05 0.005990014 -0.0007629631
## Lag 4e+05 -0.030022652 0.0194821530
## Lag 5e+05 -0.017312348 -0.0046205172
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 -0.004115470 NaN -0.01313849
## Lag 2e+05 -0.007466682 NaN 0.01621809
## Lag 3e+05 0.021488811 NaN 0.01362393
## Lag 4e+05 -0.030225442 NaN -0.00361950
## Lag 5e+05 -0.010734358 NaN -0.01635600
## nodematch.race..wa.O
## Lag 0 1.0000000000
## Lag 1e+05 0.0083226739
## Lag 2e+05 0.0149701434
## Lag 3e+05 0.0027523274
## Lag 4e+05 0.0160455149
## Lag 5e+05 0.0007560433
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0155696255 -0.003158309
## Lag 2e+05 0.0073081962 0.016221771
## Lag 3e+05 0.0324023504 -0.028866226
## Lag 4e+05 0.0020793748 0.009788450
## Lag 5e+05 -0.0006283994 0.016752136
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0060613870
## Lag 2e+05 0.0079733579
## Lag 3e+05 0.0492074440
## Lag 4e+05 -0.0001792147
## Lag 5e+05 0.0045428013
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0124513122
## Lag 2e+05 -0.0163824589
## Lag 3e+05 -0.0211378711
## Lag 4e+05 0.0307575558
## Lag 5e+05 0.0006533357
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.010345641
## Lag 2e+05 0.008247153
## Lag 3e+05 0.046402065
## Lag 4e+05 -0.005922073
## Lag 5e+05 -0.008056756
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.001327784 0.020445349
## Lag 2e+05 -0.006652760 0.017909845
## Lag 3e+05 0.014781427 0.013439996
## Lag 4e+05 0.023250075 -0.018613581
## Lag 5e+05 -0.043306963 -0.006293157
## nodefactor.race..wa.H nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 -0.006237192 NaN 0.011657077
## Lag 2e+05 -0.010931640 NaN 0.019728118
## Lag 3e+05 0.037530542 NaN 0.022795046
## Lag 4e+05 -0.007301601 NaN -0.000486767
## Lag 5e+05 0.014375116 NaN -0.024078580
## nodematch.race..wa.O
## Lag 0 1.000000000
## Lag 1e+05 0.020717617
## Lag 2e+05 0.015150776
## Lag 3e+05 0.011905116
## Lag 4e+05 0.008829357
## Lag 5e+05 -0.003141713
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.74047 0.68037
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.35816 0.07053
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.95443 -1.06205
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.81024 0.14860
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.66791
## nodematch.race..wa.O
## 0.29980
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4590175 0.4962724
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7202200 0.9437690
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3398642 0.2882123
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.4178006 0.8818678
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.5041890
## nodematch.race..wa.O
## 0.7643290
## Joint P-value (lower = worse): 0.549409 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.19000 -0.15430
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.13228 0.70728
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.16700 0.07413
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.31496 -0.09462
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.78103
## nodematch.race..wa.O
## 0.27909
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8493086 0.8773712
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.8947649 0.4793925
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.2432095 0.9409049
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7527891 0.9246147
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.4347865
## nodematch.race..wa.O
## 0.7801751
## Joint P-value (lower = worse): 0.958518 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.0325 0.8050
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3994 0.2392
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.6258 0.5612
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.0273 -0.4226
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.6717
## nodematch.race..wa.O
## 1.0231
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.3018562 0.4208115
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.6896189 0.8109877
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.5314514 0.5746642
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3042678 0.6725732
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.5017750
## nodematch.race..wa.O
## 0.3062538
## Joint P-value (lower = worse): 0.942607 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.03909 -0.35928
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.46193 -0.52027
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.03734 0.25438
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.16287 -0.56995
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.14842
## nodematch.race..wa.O
## -0.15752
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.9688170 0.7193889
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.6441334 0.6028771
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.9702111 0.7992029
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2448825 0.5687129
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.8820092
## nodematch.race..wa.O
## 0.8748362
## Joint P-value (lower = worse): 0.9895132 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.76359 0.01195
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.48513 0.47522
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.88226 -1.75650
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.68624 -0.74270
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.23717
## nodematch.race..wa.O
## -0.22617
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.44511163 0.99046505
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.62758666 0.63462895
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.37763631 0.07900388
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.49256172 0.45766349
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.81252570
## nodematch.race..wa.O
## 0.82106698
## Joint P-value (lower = worse): 0.7513384 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.3985 -0.1986
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.8862 -0.6987
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.9570 0.2141
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.6654 -1.3768
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.4450
## nodematch.race..wa.O
## 0.5314
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6902696 0.8426001
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.3754989 0.4847622
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3385450 0.8304433
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5057845 0.1685770
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.6562874
## nodematch.race..wa.O
## 0.5951139
## Joint P-value (lower = worse): 0.8754277 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7209 0.8497
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.0599 0.7305
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 2.0799 0.6182
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.5869 0.8858
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.0991
## nodematch.race..wa.O
## -0.4953
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.47097943 0.39550696
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.28917312 0.46507611
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.03753794 0.53644669
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.11253488 0.37572961
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.92105781
## nodematch.race..wa.O
## 0.62041074
## Joint P-value (lower = worse): 0.4454221 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.32079 -0.62566
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 2.04430 2.42108
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.36164 -0.09019
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.20454 0.81291
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 2.05272
## nodematch.race..wa.O
## 0.32132
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.74836731 0.53153730
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.04092354 0.01547440
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.71762244 0.92813820
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.83793289 0.41627038
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.04009959
## nodematch.race..wa.O
## 0.74796644
## Joint P-value (lower = worse): 0.2574 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 5
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -4.19363 21.797 0.12585 0.12694
## nodefactor.deg.main.deg.pers.0.1 0.54969 14.389 0.08307 0.08225
## nodefactor.deg.main.deg.pers.0.2 0.22136 6.216 0.03589 0.03589
## nodefactor.deg.main.deg.pers.1.0 -0.03198 6.191 0.03574 0.03551
## nodefactor.deg.main.deg.pers.1.1 0.72265 12.457 0.07192 0.07192
## nodefactor.deg.main.deg.pers.1.2 0.60976 12.974 0.07490 0.07414
## nodefactor.race..wa.B 6.29804 8.913 0.05146 0.05162
## nodefactor.race..wa.H 6.55839 11.060 0.06386 0.06399
## nodefactor.region.EW 0.20375 9.596 0.05540 0.05489
## nodefactor.region.OW 0.74111 17.505 0.10107 0.10059
## nodematch.race..wa.B -2.53754 0.000 0.00000 0.00000
## nodematch.race..wa.H -5.57060 2.774 0.01601 0.01619
## nodematch.race..wa.O 6.26062 17.217 0.09940 0.09856
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -46.512 -18.5122 -4.5122 10.488 38.488
## nodefactor.deg.main.deg.pers.0.1 -27.175 -9.1754 0.8246 9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217 -4.2169 -0.2169 4.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568 -4.5677 -0.5677 4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364 -7.3643 0.6357 8.636 25.636
## nodefactor.deg.main.deg.pers.1.2 -24.870 -7.8703 0.1297 9.130 26.130
## nodefactor.race..wa.B -10.186 -0.1865 5.8135 11.814 23.814
## nodefactor.race..wa.H -14.835 -0.8353 6.1647 14.165 29.165
## nodefactor.region.EW -18.389 -6.3887 -0.3887 6.611 19.611
## nodefactor.region.OW -33.159 -11.1591 0.8409 12.841 35.841
## nodematch.race..wa.B -2.538 -2.5375 -2.5375 -2.538 -2.538
## nodematch.race..wa.H -10.275 -7.2750 -5.2750 -4.275 0.725
## nodematch.race..wa.O -26.884 -5.8841 6.1159 18.116 40.116
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5652485
## nodefactor.deg.main.deg.pers.0.2 0.2822276
## nodefactor.deg.main.deg.pers.1.0 0.2732382
## nodefactor.deg.main.deg.pers.1.1 0.4991270
## nodefactor.deg.main.deg.pers.1.2 0.5104651
## nodefactor.race..wa.B 0.4093834
## nodefactor.race..wa.H 0.4421720
## nodefactor.region.EW 0.4039847
## nodefactor.region.OW 0.6369959
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.1293270
## nodematch.race..wa.O 0.7908798
## nodefactor.deg.main.deg.pers.0.1
## edges 0.56524848
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.08007919
## nodefactor.deg.main.deg.pers.1.0 0.07858225
## nodefactor.deg.main.deg.pers.1.1 0.14014273
## nodefactor.deg.main.deg.pers.1.2 0.14067499
## nodefactor.race..wa.B 0.24494320
## nodefactor.race..wa.H 0.24081257
## nodefactor.region.EW 0.23909230
## nodefactor.region.OW 0.37188296
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.07312426
## nodematch.race..wa.O 0.44589948
## nodefactor.deg.main.deg.pers.0.2
## edges 0.28222760
## nodefactor.deg.main.deg.pers.0.1 0.08007919
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03041187
## nodefactor.deg.main.deg.pers.1.1 0.07710859
## nodefactor.deg.main.deg.pers.1.2 0.07006828
## nodefactor.race..wa.B 0.12769306
## nodefactor.race..wa.H 0.11044998
## nodefactor.region.EW 0.11255732
## nodefactor.region.OW 0.17916971
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03398996
## nodematch.race..wa.O 0.22572614
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27323816
## nodefactor.deg.main.deg.pers.0.1 0.07858225
## nodefactor.deg.main.deg.pers.0.2 0.03041187
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.07852927
## nodefactor.deg.main.deg.pers.1.2 0.06897309
## nodefactor.race..wa.B 0.10114552
## nodefactor.race..wa.H 0.12789937
## nodefactor.region.EW 0.10364036
## nodefactor.region.OW 0.16192582
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03940884
## nodematch.race..wa.O 0.21775180
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49912702
## nodefactor.deg.main.deg.pers.0.1 0.14014273
## nodefactor.deg.main.deg.pers.0.2 0.07710859
## nodefactor.deg.main.deg.pers.1.0 0.07852927
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.11909869
## nodefactor.race..wa.B 0.18374560
## nodefactor.race..wa.H 0.20952060
## nodefactor.region.EW 0.18254195
## nodefactor.region.OW 0.27577812
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.05473472
## nodematch.race..wa.O 0.41100812
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51046508
## nodefactor.deg.main.deg.pers.0.1 0.14067499
## nodefactor.deg.main.deg.pers.0.2 0.07006828
## nodefactor.deg.main.deg.pers.1.0 0.06897309
## nodefactor.deg.main.deg.pers.1.1 0.11909869
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.18986350
## nodefactor.race..wa.H 0.26599005
## nodefactor.region.EW 0.22166579
## nodefactor.region.OW 0.31766397
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.09371512
## nodematch.race..wa.O 0.39219894
## nodefactor.race..wa.B
## edges 0.4093833672
## nodefactor.deg.main.deg.pers.0.1 0.2449432006
## nodefactor.deg.main.deg.pers.0.2 0.1276930582
## nodefactor.deg.main.deg.pers.1.0 0.1011455214
## nodefactor.deg.main.deg.pers.1.1 0.1837456016
## nodefactor.deg.main.deg.pers.1.2 0.1898634956
## nodefactor.race..wa.B 1.0000000000
## nodefactor.race..wa.H -0.0033484239
## nodefactor.region.EW 0.0979253367
## nodefactor.region.OW 0.2385142156
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.0005923706
## nodematch.race..wa.O 0.0028577450
## nodefactor.race..wa.H
## edges 4.421720e-01
## nodefactor.deg.main.deg.pers.0.1 2.408126e-01
## nodefactor.deg.main.deg.pers.0.2 1.104500e-01
## nodefactor.deg.main.deg.pers.1.0 1.278994e-01
## nodefactor.deg.main.deg.pers.1.1 2.095206e-01
## nodefactor.deg.main.deg.pers.1.2 2.659901e-01
## nodefactor.race..wa.B -3.348424e-03
## nodefactor.race..wa.H 1.000000e+00
## nodefactor.region.EW 2.965637e-01
## nodefactor.region.OW 2.674665e-01
## nodematch.race..wa.B NA
## nodematch.race..wa.H 5.025899e-01
## nodematch.race..wa.O 9.944499e-05
## nodefactor.region.EW nodefactor.region.OW
## edges 0.40398474 0.63699592
## nodefactor.deg.main.deg.pers.0.1 0.23909230 0.37188296
## nodefactor.deg.main.deg.pers.0.2 0.11255732 0.17916971
## nodefactor.deg.main.deg.pers.1.0 0.10364036 0.16192582
## nodefactor.deg.main.deg.pers.1.1 0.18254195 0.27577812
## nodefactor.deg.main.deg.pers.1.2 0.22166579 0.31766397
## nodefactor.race..wa.B 0.09792534 0.23851422
## nodefactor.race..wa.H 0.29656375 0.26746652
## nodefactor.region.EW 1.00000000 0.12443976
## nodefactor.region.OW 0.12443976 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.11292622 0.07673365
## nodematch.race..wa.O 0.28844067 0.52352072
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.1293270415
## nodefactor.deg.main.deg.pers.0.1 NA 0.0731242571
## nodefactor.deg.main.deg.pers.0.2 NA 0.0339899632
## nodefactor.deg.main.deg.pers.1.0 NA 0.0394088380
## nodefactor.deg.main.deg.pers.1.1 NA 0.0547347244
## nodefactor.deg.main.deg.pers.1.2 NA 0.0937151211
## nodefactor.race..wa.B NA 0.0005923706
## nodefactor.race..wa.H NA 0.5025899479
## nodefactor.region.EW NA 0.1129262211
## nodefactor.region.OW NA 0.0767336478
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.0000000000
## nodematch.race..wa.O NA 0.0016622237
## nodematch.race..wa.O
## edges 7.908798e-01
## nodefactor.deg.main.deg.pers.0.1 4.458995e-01
## nodefactor.deg.main.deg.pers.0.2 2.257261e-01
## nodefactor.deg.main.deg.pers.1.0 2.177518e-01
## nodefactor.deg.main.deg.pers.1.1 4.110081e-01
## nodefactor.deg.main.deg.pers.1.2 3.921989e-01
## nodefactor.race..wa.B 2.857745e-03
## nodefactor.race..wa.H 9.944499e-05
## nodefactor.region.EW 2.884407e-01
## nodefactor.region.OW 5.235207e-01
## nodematch.race..wa.B NA
## nodematch.race..wa.H 1.662224e-03
## nodematch.race..wa.O 1.000000e+00
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.004408803 0.021847213
## Lag 2e+05 0.011046968 0.009593305
## Lag 3e+05 0.008435084 -0.012220016
## Lag 4e+05 -0.032557670 -0.003099623
## Lag 5e+05 -0.002756434 0.016223078
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.009132877
## Lag 2e+05 -0.002049675
## Lag 3e+05 -0.014425970
## Lag 4e+05 -0.018530502
## Lag 5e+05 0.008662617
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.004627539
## Lag 2e+05 -0.016583603
## Lag 3e+05 -0.019071780
## Lag 4e+05 -0.009837854
## Lag 5e+05 -0.004153599
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.008800654
## Lag 2e+05 0.015673851
## Lag 3e+05 0.023297830
## Lag 4e+05 0.006933801
## Lag 5e+05 0.002073913
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.009483402 0.006183689
## Lag 2e+05 0.004909858 0.003312812
## Lag 3e+05 -0.007265956 -0.013443996
## Lag 4e+05 -0.014384301 -0.012995048
## Lag 5e+05 0.012858449 -0.008858417
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0006230853 -0.020406211 0.006352770
## Lag 2e+05 -0.0184831919 0.018377004 -0.021652561
## Lag 3e+05 -0.0218443394 -0.002652334 0.007622164
## Lag 4e+05 -0.0028031735 0.005413128 -0.012284751
## Lag 5e+05 -0.0037315048 -0.011030192 -0.019982387
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.001189914 -0.003794545
## Lag 2e+05 NaN -0.012999648 0.013005050
## Lag 3e+05 NaN -0.007251447 0.022305232
## Lag 4e+05 NaN 0.025399585 -0.009483293
## Lag 5e+05 NaN 0.018932433 -0.006150889
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.013144899 0.004181882
## Lag 2e+05 -0.022440569 -0.031057328
## Lag 3e+05 0.004206147 -0.027259651
## Lag 4e+05 -0.007478419 -0.010749075
## Lag 5e+05 -0.008059571 -0.016983572
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.001409496
## Lag 2e+05 0.021181523
## Lag 3e+05 -0.009296457
## Lag 4e+05 -0.013567982
## Lag 5e+05 -0.035850198
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.027582621
## Lag 2e+05 0.017004925
## Lag 3e+05 0.017326475
## Lag 4e+05 -0.007874591
## Lag 5e+05 0.010241195
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.003854792
## Lag 2e+05 -0.002638751
## Lag 3e+05 -0.008402796
## Lag 4e+05 0.002498310
## Lag 5e+05 0.013213020
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.024077915 0.011004571
## Lag 2e+05 -0.013272267 0.013927954
## Lag 3e+05 0.011180615 0.015088593
## Lag 4e+05 -0.002072093 -0.008595558
## Lag 5e+05 0.003320106 0.009370411
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 -0.01854612 -0.002904527 -0.034801828
## Lag 2e+05 0.01543778 -0.010767616 -0.010705323
## Lag 3e+05 0.01204741 -0.011175965 0.006876874
## Lag 4e+05 0.01566985 0.019536280 0.016846473
## Lag 5e+05 0.01686364 0.006145531 -0.024580472
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.00000000
## Lag 1e+05 NaN 0.033077212 0.01216770
## Lag 2e+05 NaN -0.001662741 -0.01929117
## Lag 3e+05 NaN -0.020688167 -0.01076396
## Lag 4e+05 NaN 0.021044733 -0.02634214
## Lag 5e+05 NaN 0.020721525 -0.02292867
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.013936926 0.022966090
## Lag 2e+05 -0.002766157 -0.016000510
## Lag 3e+05 -0.007366110 -0.005355045
## Lag 4e+05 0.003258313 -0.008277261
## Lag 5e+05 0.012099846 -0.002604064
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0004492531
## Lag 2e+05 0.0038336181
## Lag 3e+05 0.0267935986
## Lag 4e+05 0.0205876458
## Lag 5e+05 -0.0110025298
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.009891949
## Lag 2e+05 0.005964676
## Lag 3e+05 0.005430987
## Lag 4e+05 0.004155003
## Lag 5e+05 0.014855460
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.007216891
## Lag 2e+05 -0.008699936
## Lag 3e+05 0.007168501
## Lag 4e+05 0.015354878
## Lag 5e+05 0.002635049
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.009205539 0.002876198
## Lag 2e+05 0.003265131 0.008308411
## Lag 3e+05 0.013656536 -0.020596449
## Lag 4e+05 -0.024948854 0.003057415
## Lag 5e+05 -0.015905562 -0.006800265
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.027744710 -0.0156982622 0.0229332622
## Lag 2e+05 0.022315422 0.0005044831 0.0003987712
## Lag 3e+05 -0.034002649 -0.0149960716 0.0088896371
## Lag 4e+05 0.008439452 0.0006722239 0.0135233094
## Lag 5e+05 0.024053997 -0.0081178627 0.0279940606
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.012678015 0.032344707
## Lag 2e+05 NaN 0.013293651 -0.004247220
## Lag 3e+05 NaN -0.003148378 0.008890546
## Lag 4e+05 NaN -0.011061385 -0.009285504
## Lag 5e+05 NaN -0.007372540 -0.005422188
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.017569789 0.0135810639
## Lag 2e+05 -0.002089649 0.0002329469
## Lag 3e+05 0.027926403 0.0156160304
## Lag 4e+05 0.004628454 -0.0010185090
## Lag 5e+05 0.015290693 0.0074772258
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.005140564
## Lag 2e+05 -0.004002997
## Lag 3e+05 -0.003463366
## Lag 4e+05 0.009630499
## Lag 5e+05 -0.018558418
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0153498465
## Lag 2e+05 0.0006118448
## Lag 3e+05 0.0107360979
## Lag 4e+05 -0.0346214797
## Lag 5e+05 -0.0008597004
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.013633392
## Lag 2e+05 0.008827248
## Lag 3e+05 0.031279550
## Lag 4e+05 0.010085732
## Lag 5e+05 -0.010256584
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.026936205 -0.035963699
## Lag 2e+05 -0.010674413 -0.004637310
## Lag 3e+05 0.012082565 0.004951477
## Lag 4e+05 0.001603205 -0.007354444
## Lag 5e+05 -0.005273472 0.002668715
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.015578317 -0.024449665 0.012238604
## Lag 2e+05 0.014247679 -0.009822856 0.001376408
## Lag 3e+05 0.008027207 0.012862921 -0.001744527
## Lag 4e+05 0.013707834 -0.004317860 -0.009054175
## Lag 5e+05 0.014378099 -0.010928463 0.024052224
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.022429539 -0.017130136
## Lag 2e+05 NaN 0.006529393 -0.017601085
## Lag 3e+05 NaN 0.000148419 0.014485984
## Lag 4e+05 NaN -0.018147344 -0.003376153
## Lag 5e+05 NaN -0.005658106 0.013440723
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.023291773 0.02129490
## Lag 2e+05 0.005619267 0.02255089
## Lag 3e+05 0.009454548 0.01003475
## Lag 4e+05 -0.008629484 0.01125333
## Lag 5e+05 0.018959134 0.00532500
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0064496859
## Lag 2e+05 -0.0002627091
## Lag 3e+05 -0.0207196078
## Lag 4e+05 -0.0141465804
## Lag 5e+05 0.0125271296
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.007728082
## Lag 2e+05 0.013935317
## Lag 3e+05 0.002155501
## Lag 4e+05 0.002840733
## Lag 5e+05 0.009067254
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.008132107
## Lag 2e+05 -0.027586770
## Lag 3e+05 0.026154416
## Lag 4e+05 0.001840309
## Lag 5e+05 -0.005258293
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.008972445 0.029883283
## Lag 2e+05 0.003220724 0.023141973
## Lag 3e+05 -0.001512831 0.004760106
## Lag 4e+05 -0.020389777 0.022140817
## Lag 5e+05 0.002913525 0.035485689
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0154588863 0.007752573 -0.005303602
## Lag 2e+05 0.0011179697 0.018975278 0.011257456
## Lag 3e+05 0.0099664453 -0.003761075 0.037622204
## Lag 4e+05 -0.0072500278 -0.018102431 -0.010646220
## Lag 5e+05 -0.0002733342 0.006710798 0.016824524
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.013029322 0.020519988
## Lag 2e+05 NaN 0.038707870 0.001196092
## Lag 3e+05 NaN -0.019445718 0.001194106
## Lag 4e+05 NaN -0.024348948 -0.012724743
## Lag 5e+05 NaN -0.009790533 0.007654802
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 -0.016635594 0.0035368986
## Lag 2e+05 0.035520925 0.0174808992
## Lag 3e+05 -0.004706651 0.0005283090
## Lag 4e+05 0.005123158 0.0009582252
## Lag 5e+05 0.003282313 -0.0205966667
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0051083065
## Lag 2e+05 -0.0056848867
## Lag 3e+05 -0.0004500454
## Lag 4e+05 0.0045843106
## Lag 5e+05 -0.0093713703
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.009804727
## Lag 2e+05 -0.009405320
## Lag 3e+05 -0.047188844
## Lag 4e+05 -0.022465323
## Lag 5e+05 -0.015915437
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.0000000000
## Lag 1e+05 0.0187065725
## Lag 2e+05 0.0149505617
## Lag 3e+05 0.0005554036
## Lag 4e+05 -0.0125951969
## Lag 5e+05 0.0187216374
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.031875033 -0.0041998029
## Lag 2e+05 -0.012365191 0.0005497124
## Lag 3e+05 -0.006567242 -0.0106279851
## Lag 4e+05 0.003806141 -0.0159248889
## Lag 5e+05 -0.009073454 0.0031138183
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.0000000000
## Lag 1e+05 0.002542687 0.0141455794 -0.0214357905
## Lag 2e+05 0.008720920 0.0007538431 -0.0008994087
## Lag 3e+05 -0.021242283 0.0124842672 0.0036151554
## Lag 4e+05 -0.001461241 0.0075220754 0.0307924899
## Lag 5e+05 0.006751540 0.0111326449 -0.0129430017
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.004179914 -0.010066814
## Lag 2e+05 NaN 0.015485236 0.038138906
## Lag 3e+05 NaN 0.016421090 -0.005318257
## Lag 4e+05 NaN 0.008770505 0.003346465
## Lag 5e+05 NaN -0.036918514 0.013229124
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.025876561 0.0249993463
## Lag 2e+05 -0.021736530 -0.0151130029
## Lag 3e+05 0.008337053 0.0009708044
## Lag 4e+05 0.029666430 0.0168255059
## Lag 5e+05 0.017682570 -0.0162595623
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.00000000
## Lag 1e+05 -0.01861627
## Lag 2e+05 -0.01041266
## Lag 3e+05 -0.02206965
## Lag 4e+05 -0.01547276
## Lag 5e+05 0.01236251
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.008837403
## Lag 2e+05 0.017624716
## Lag 3e+05 -0.016431411
## Lag 4e+05 -0.020810861
## Lag 5e+05 0.020673398
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.008617843
## Lag 2e+05 0.002933812
## Lag 3e+05 0.012042560
## Lag 4e+05 0.021582231
## Lag 5e+05 -0.009858680
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.007857164 0.030279458
## Lag 2e+05 -0.015781244 0.002009358
## Lag 3e+05 -0.012777915 0.012606891
## Lag 4e+05 0.023141370 0.008335398
## Lag 5e+05 -0.006583357 -0.005913246
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.0000000000
## Lag 1e+05 0.0007588585 -0.002575717 -0.0001369712
## Lag 2e+05 -0.0204936581 -0.022554016 -0.0167178602
## Lag 3e+05 0.0074964145 -0.001927445 0.0011314721
## Lag 4e+05 0.0095639671 0.001487305 0.0134915700
## Lag 5e+05 -0.0263685702 0.005432186 0.0083925457
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.005046048 0.020104910
## Lag 2e+05 NaN 0.007571232 -0.031510381
## Lag 3e+05 NaN 0.014302437 -0.008963648
## Lag 4e+05 NaN 0.003617738 0.015684908
## Lag 5e+05 NaN 0.001082283 0.020813809
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.014639116 0.028210719
## Lag 2e+05 -0.008063000 -0.001019846
## Lag 3e+05 -0.002075622 0.011135835
## Lag 4e+05 -0.017873309 0.007768939
## Lag 5e+05 -0.008242085 -0.013852857
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.012296959
## Lag 2e+05 -0.001805836
## Lag 3e+05 0.007299742
## Lag 4e+05 -0.012963159
## Lag 5e+05 -0.016905106
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003650985
## Lag 2e+05 0.009991197
## Lag 3e+05 -0.022123460
## Lag 4e+05 -0.002884759
## Lag 5e+05 0.008076035
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.008761188
## Lag 2e+05 -0.022203550
## Lag 3e+05 0.006645297
## Lag 4e+05 -0.002267879
## Lag 5e+05 -0.018318936
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.002879269 -0.01697975
## Lag 2e+05 -0.035994758 -0.01539481
## Lag 3e+05 -0.032905029 -0.01421289
## Lag 4e+05 -0.001715361 0.01085179
## Lag 5e+05 -0.001957092 0.02587172
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.030214854 0.01402675 0.00589752
## Lag 2e+05 -0.003769400 -0.03647840 -0.02113074
## Lag 3e+05 -0.007614316 -0.03030370 0.03521800
## Lag 4e+05 -0.002395037 0.01508648 -0.02435567
## Lag 5e+05 -0.040582727 -0.00468781 -0.01656606
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.0000000000 1.000000000
## Lag 1e+05 NaN -0.0112326093 0.001366477
## Lag 2e+05 NaN -0.0009367922 -0.014942297
## Lag 3e+05 NaN 0.0086593838 0.006532705
## Lag 4e+05 NaN 0.0013814051 -0.013476941
## Lag 5e+05 NaN -0.0057276928 -0.023947070
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.60859 -1.10954
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.26383 -0.60850
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.81034 1.89054
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.94175 0.64537
## nodefactor.region.EW nodefactor.region.OW
## 0.32950 -0.09244
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.64982
## nodematch.race..wa.O
## -0.77013
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.54279692 0.26719777
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.79191336 0.54285658
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.07024357 0.05868552
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.05216715 0.51868836
## nodefactor.region.EW nodefactor.region.OW
## 0.74177859 0.92634498
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.51580797
## nodematch.race..wa.O
## 0.44122220
## Joint P-value (lower = worse): 0.2205978 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.67081 -0.64046
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.71421 2.21612
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.47737 -0.61173
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.57395 0.70488
## nodefactor.region.EW nodefactor.region.OW
## -0.05633 0.73853
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.55437
## nodematch.race..wa.O
## 0.21290
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.50234084 0.52187569
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.47509448 0.02668299
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.13957734 0.54071472
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.56600070 0.48088719
## nodefactor.region.EW nodefactor.region.OW
## 0.95507908 0.46019033
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.57932285
## nodematch.race..wa.O
## 0.83140180
## Joint P-value (lower = worse): 0.6484416 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.9191 -0.9312
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.0377 -0.9880
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.5420 -0.6554
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.3438 -0.9422
## nodefactor.region.EW nodefactor.region.OW
## -0.7808 -0.3044
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.8507
## nodematch.race..wa.O
## -0.7713
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.05496751 0.35172459
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.29940178 0.32316571
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.58781494 0.51218582
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.17900061 0.34608384
## nodefactor.region.EW nodefactor.region.OW
## 0.43492037 0.76085973
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.39491687
## nodematch.race..wa.O
## 0.44054766
## Joint P-value (lower = worse): 0.430132 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.372015 1.089303
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.977236 -0.225081
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.436118 -0.400388
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.369137 1.825629
## nodefactor.region.EW nodefactor.region.OW
## 0.776413 2.559636
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.006441
## nodematch.race..wa.O
## 0.333266
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.17005867 0.27602043
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.32845220 0.82191601
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.15096889 0.68887085
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.71202562 0.06790620
## nodefactor.region.EW nodefactor.region.OW
## 0.43750534 0.01047819
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.99486091
## nodematch.race..wa.O
## 0.73893386
## Joint P-value (lower = worse): 0.2544948 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8249 1.4951
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.5051 -2.2141
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4892 -0.4064
## nodefactor.race..wa.B nodefactor.race..wa.H
## 2.0592 -1.1586
## nodefactor.region.EW nodefactor.region.OW
## -1.0370 -0.1444
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -1.4224
## nodematch.race..wa.O
## 0.4269
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.40945307 0.13487624
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.61347963 0.02681890
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.62471341 0.68445847
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.03947741 0.24663588
## nodefactor.region.EW nodefactor.region.OW
## 0.29973788 0.88519853
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.15489975
## nodematch.race..wa.O
## 0.66948021
## Joint P-value (lower = worse): 0.1243811 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4859 1.0706
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.3455 0.1330
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.9766 -0.6670
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.1664 0.7706
## nodefactor.region.EW nodefactor.region.OW
## -0.2788 -1.0773
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 1.5546
## nodematch.race..wa.O
## 0.4372
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.62702420 0.28435051
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.01900354 0.89423091
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.32875612 0.50475061
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.86785301 0.44095438
## nodefactor.region.EW nodefactor.region.OW
## 0.78038910 0.28132958
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.12004513
## nodematch.race..wa.O
## 0.66199957
## Joint P-value (lower = worse): 0.2285471 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.0682 0.3026
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.6594 0.3259
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7779 -0.5568
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.9392 -0.4104
## nodefactor.region.EW nodefactor.region.OW
## 0.5429 1.1518
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.2005
## nodematch.race..wa.O
## 0.8721
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.94562716 0.76221234
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.09703139 0.74447281
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.43664898 0.57767569
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.34761559 0.68154302
## nodefactor.region.EW nodefactor.region.OW
## 0.58720646 0.24939804
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.84111171
## nodematch.race..wa.O
## 0.38312780
## Joint P-value (lower = worse): 0.6252442 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.0679 1.1085
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.2375 -0.8394
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.6268 0.7491
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8530 0.1264
## nodefactor.region.EW nodefactor.region.OW
## 1.6602 0.6473
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 1.5318
## nodematch.race..wa.O
## 1.0357
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.28558292 0.26765800
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.81223080 0.40125011
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.53081414 0.45378229
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.39367010 0.89944978
## nodefactor.region.EW nodefactor.region.OW
## 0.09686806 0.51741036
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.12557800
## nodematch.race..wa.O
## 0.30034038
## Joint P-value (lower = worse): 0.6236519 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 6
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -5.40403 21.703 0.12530 0.12319
## nodefactor.deg.main.deg.pers.0.1 0.06846 14.218 0.08209 0.08175
## nodefactor.deg.main.deg.pers.0.2 -0.03144 6.247 0.03607 0.03601
## nodefactor.deg.main.deg.pers.1.0 -0.07208 6.128 0.03538 0.03510
## nodefactor.deg.main.deg.pers.1.1 0.17571 12.357 0.07134 0.07164
## nodefactor.deg.main.deg.pers.1.2 0.12369 12.927 0.07463 0.07420
## nodefactor.race..wa.B 6.03937 8.983 0.05186 0.05165
## nodefactor.race..wa.H 5.64675 10.947 0.06321 0.06310
## nodefactor.region.EW 0.07621 9.644 0.05568 0.05540
## nodefactor.region.OW 0.07544 17.389 0.10039 0.09985
## nodematch.race..wa.B -2.53754 0.000 0.00000 0.00000
## nodematch.race..wa.H -5.73306 2.742 0.01583 0.01586
## nodematch.race..wa.O 6.05805 17.081 0.09862 0.09841
## absdiff.sqrt.age 0.31252 22.793 0.13160 0.13473
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -47.512 -20.5122 -5.51216 9.488 37.488
## nodefactor.deg.main.deg.pers.0.1 -27.175 -10.1754 -0.17541 9.825 28.825
## nodefactor.deg.main.deg.pers.0.2 -11.217 -4.2169 -0.21687 3.783 12.783
## nodefactor.deg.main.deg.pers.1.0 -11.568 -4.5677 -0.56768 4.432 12.432
## nodefactor.deg.main.deg.pers.1.1 -23.364 -8.3643 -0.36432 8.636 24.636
## nodefactor.deg.main.deg.pers.1.2 -24.870 -8.8703 0.12966 9.130 26.130
## nodefactor.race..wa.B -11.186 -0.1865 5.81350 11.814 23.814
## nodefactor.race..wa.H -14.835 -1.8353 5.16472 13.165 28.165
## nodefactor.region.EW -18.389 -6.3887 -0.38872 6.611 19.611
## nodefactor.region.OW -34.159 -12.1591 -0.15906 11.841 34.841
## nodematch.race..wa.B -2.538 -2.5375 -2.53754 -2.538 -2.538
## nodematch.race..wa.H -10.275 -7.2750 -6.27496 -4.275 -0.275
## nodematch.race..wa.O -26.884 -5.8841 6.11592 17.116 40.116
## absdiff.sqrt.age -43.536 -15.3242 0.09694 15.562 45.333
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5534125
## nodefactor.deg.main.deg.pers.0.2 0.2791760
## nodefactor.deg.main.deg.pers.1.0 0.2720727
## nodefactor.deg.main.deg.pers.1.1 0.4988419
## nodefactor.deg.main.deg.pers.1.2 0.5146578
## nodefactor.race..wa.B 0.4093242
## nodefactor.race..wa.H 0.4464807
## nodefactor.region.EW 0.4028496
## nodefactor.region.OW 0.6375991
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.1238437
## nodematch.race..wa.O 0.7890364
## absdiff.sqrt.age 0.7671644
## nodefactor.deg.main.deg.pers.0.1
## edges 0.55341252
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07048945
## nodefactor.deg.main.deg.pers.1.0 0.06793310
## nodefactor.deg.main.deg.pers.1.1 0.14121115
## nodefactor.deg.main.deg.pers.1.2 0.13743781
## nodefactor.race..wa.B 0.24951222
## nodefactor.race..wa.H 0.23472613
## nodefactor.region.EW 0.22733061
## nodefactor.region.OW 0.35941302
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.06049134
## nodematch.race..wa.O 0.43120473
## absdiff.sqrt.age 0.42595671
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27917599
## nodefactor.deg.main.deg.pers.0.1 0.07048945
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.04654241
## nodefactor.deg.main.deg.pers.1.1 0.07590497
## nodefactor.deg.main.deg.pers.1.2 0.06752506
## nodefactor.race..wa.B 0.12715915
## nodefactor.race..wa.H 0.11535565
## nodefactor.region.EW 0.11160003
## nodefactor.region.OW 0.17732873
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.02367346
## nodematch.race..wa.O 0.21770820
## absdiff.sqrt.age 0.21585259
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27207275
## nodefactor.deg.main.deg.pers.0.1 0.06793310
## nodefactor.deg.main.deg.pers.0.2 0.04654241
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.06548288
## nodefactor.deg.main.deg.pers.1.2 0.06296366
## nodefactor.race..wa.B 0.09433379
## nodefactor.race..wa.H 0.13207342
## nodefactor.region.EW 0.10815694
## nodefactor.region.OW 0.15555399
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03940024
## nodematch.race..wa.O 0.21775621
## absdiff.sqrt.age 0.20700790
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49884186
## nodefactor.deg.main.deg.pers.0.1 0.14121115
## nodefactor.deg.main.deg.pers.0.2 0.07590497
## nodefactor.deg.main.deg.pers.1.0 0.06548288
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13105309
## nodefactor.race..wa.B 0.17348717
## nodefactor.race..wa.H 0.21928094
## nodefactor.region.EW 0.18220771
## nodefactor.region.OW 0.28134953
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.05192828
## nodematch.race..wa.O 0.41037588
## absdiff.sqrt.age 0.38816937
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51465778
## nodefactor.deg.main.deg.pers.0.1 0.13743781
## nodefactor.deg.main.deg.pers.0.2 0.06752506
## nodefactor.deg.main.deg.pers.1.0 0.06296366
## nodefactor.deg.main.deg.pers.1.1 0.13105309
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.race..wa.B 0.19303486
## nodefactor.race..wa.H 0.26624389
## nodefactor.region.EW 0.21341463
## nodefactor.region.OW 0.31495122
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.08450180
## nodematch.race..wa.O 0.39531989
## absdiff.sqrt.age 0.39238369
## nodefactor.race..wa.B
## edges 0.409324211
## nodefactor.deg.main.deg.pers.0.1 0.249512217
## nodefactor.deg.main.deg.pers.0.2 0.127159147
## nodefactor.deg.main.deg.pers.1.0 0.094333793
## nodefactor.deg.main.deg.pers.1.1 0.173487166
## nodefactor.deg.main.deg.pers.1.2 0.193034859
## nodefactor.race..wa.B 1.000000000
## nodefactor.race..wa.H -0.000790199
## nodefactor.region.EW 0.095902868
## nodefactor.region.OW 0.244708877
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.008461564
## nodematch.race..wa.O -0.003976878
## absdiff.sqrt.age 0.314201029
## nodefactor.race..wa.H
## edges 0.446480720
## nodefactor.deg.main.deg.pers.0.1 0.234726128
## nodefactor.deg.main.deg.pers.0.2 0.115355649
## nodefactor.deg.main.deg.pers.1.0 0.132073422
## nodefactor.deg.main.deg.pers.1.1 0.219280936
## nodefactor.deg.main.deg.pers.1.2 0.266243889
## nodefactor.race..wa.B -0.000790199
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.292086365
## nodefactor.region.OW 0.270224399
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.495077649
## nodematch.race..wa.O 0.006256945
## absdiff.sqrt.age 0.341708946
## nodefactor.region.EW nodefactor.region.OW
## edges 0.40284961 0.63759906
## nodefactor.deg.main.deg.pers.0.1 0.22733061 0.35941302
## nodefactor.deg.main.deg.pers.0.2 0.11160003 0.17732873
## nodefactor.deg.main.deg.pers.1.0 0.10815694 0.15555399
## nodefactor.deg.main.deg.pers.1.1 0.18220771 0.28134953
## nodefactor.deg.main.deg.pers.1.2 0.21341463 0.31495122
## nodefactor.race..wa.B 0.09590287 0.24470888
## nodefactor.race..wa.H 0.29208637 0.27022440
## nodefactor.region.EW 1.00000000 0.13056121
## nodefactor.region.OW 0.13056121 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.11268539 0.07293986
## nodematch.race..wa.O 0.29230264 0.51994413
## absdiff.sqrt.age 0.30563097 0.49312397
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.123843703
## nodefactor.deg.main.deg.pers.0.1 NA 0.060491335
## nodefactor.deg.main.deg.pers.0.2 NA 0.023673462
## nodefactor.deg.main.deg.pers.1.0 NA 0.039400238
## nodefactor.deg.main.deg.pers.1.1 NA 0.051928279
## nodefactor.deg.main.deg.pers.1.2 NA 0.084501802
## nodefactor.race..wa.B NA 0.008461564
## nodefactor.race..wa.H NA 0.495077649
## nodefactor.region.EW NA 0.112685391
## nodefactor.region.OW NA 0.072939862
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA -0.003882979
## absdiff.sqrt.age NA 0.086745194
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.789036404 0.76716437
## nodefactor.deg.main.deg.pers.0.1 0.431204727 0.42595671
## nodefactor.deg.main.deg.pers.0.2 0.217708198 0.21585259
## nodefactor.deg.main.deg.pers.1.0 0.217756213 0.20700790
## nodefactor.deg.main.deg.pers.1.1 0.410375881 0.38816937
## nodefactor.deg.main.deg.pers.1.2 0.395319890 0.39238369
## nodefactor.race..wa.B -0.003976878 0.31420103
## nodefactor.race..wa.H 0.006256945 0.34170895
## nodefactor.region.EW 0.292302636 0.30563097
## nodefactor.region.OW 0.519944130 0.49312397
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H -0.003882979 0.08674519
## nodematch.race..wa.O 1.000000000 0.60442090
## absdiff.sqrt.age 0.604420901 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.007056281 -0.002785231
## Lag 2e+05 -0.021135262 -0.013152720
## Lag 3e+05 0.025422518 -0.014344615
## Lag 4e+05 0.010293741 -0.024978414
## Lag 5e+05 0.027377325 0.023155697
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.000265926
## Lag 2e+05 -0.011634818
## Lag 3e+05 0.002687452
## Lag 4e+05 -0.011767352
## Lag 5e+05 0.000633360
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.017490733
## Lag 2e+05 -0.004581797
## Lag 3e+05 0.011310593
## Lag 4e+05 0.028462300
## Lag 5e+05 -0.012748426
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.012134856
## Lag 2e+05 -0.011723910
## Lag 3e+05 -0.014322647
## Lag 4e+05 -0.001840124
## Lag 5e+05 -0.005448570
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0289584318 -0.002959644
## Lag 2e+05 0.0104946696 -0.023447098
## Lag 3e+05 0.0050468198 0.005869192
## Lag 4e+05 -0.0005164998 0.026338513
## Lag 5e+05 0.0299984175 0.014288048
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.021150237 0.003605998 0.00660325
## Lag 2e+05 -0.022351367 -0.029485699 -0.01712849
## Lag 3e+05 0.011731415 0.034368607 0.01934329
## Lag 4e+05 0.015150003 -0.022074196 -0.00183978
## Lag 5e+05 0.001786736 0.010480002 0.02885417
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.027902380 0.021164667
## Lag 2e+05 NaN 0.008924406 -0.005366982
## Lag 3e+05 NaN 0.029479542 0.021864756
## Lag 4e+05 NaN -0.009325174 0.013878885
## Lag 5e+05 NaN -0.014867966 0.019561425
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.002029149
## Lag 2e+05 -0.018664590
## Lag 3e+05 0.012974076
## Lag 4e+05 0.039865232
## Lag 5e+05 0.046059834
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.01213031 0.016310015
## Lag 2e+05 0.01858269 0.005339163
## Lag 3e+05 -0.01094819 -0.006916254
## Lag 4e+05 0.01638422 0.040457066
## Lag 5e+05 0.02707308 0.017786115
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.013371001
## Lag 2e+05 -0.002777669
## Lag 3e+05 0.015754713
## Lag 4e+05 0.016071698
## Lag 5e+05 -0.029246554
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.028216429
## Lag 2e+05 0.002224599
## Lag 3e+05 -0.019195511
## Lag 4e+05 0.003584746
## Lag 5e+05 0.008798429
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.001963151
## Lag 2e+05 0.006739325
## Lag 3e+05 -0.001021309
## Lag 4e+05 0.006466508
## Lag 5e+05 0.033441550
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.015258568 -0.005245486
## Lag 2e+05 -0.019522991 -0.004154377
## Lag 3e+05 -0.019743377 0.020572493
## Lag 4e+05 -0.008525181 0.005177351
## Lag 5e+05 0.026378243 -0.024835528
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0091514729 -0.008486691 -0.004004691
## Lag 2e+05 0.0106896118 -0.002338231 0.010767132
## Lag 3e+05 -0.0068863199 0.012321873 -0.001338476
## Lag 4e+05 -0.0005001239 0.028064680 0.014052673
## Lag 5e+05 0.0035271383 0.008949685 -0.015200181
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.001862073 0.001817307
## Lag 2e+05 NaN -0.028072237 0.032778775
## Lag 3e+05 NaN 0.006960506 -0.027174320
## Lag 4e+05 NaN -0.004372304 0.007466263
## Lag 5e+05 NaN -0.015003321 0.003986580
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.025845116
## Lag 2e+05 0.022889168
## Lag 3e+05 -0.016400514
## Lag 4e+05 0.005056477
## Lag 5e+05 0.026331855
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 -0.02644799 0.016162996
## Lag 2e+05 -0.01672671 -0.014496743
## Lag 3e+05 -0.01465317 -0.015783141
## Lag 4e+05 -0.01954231 0.007744968
## Lag 5e+05 -0.03875416 -0.002296934
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0406229061
## Lag 2e+05 0.0009988785
## Lag 3e+05 -0.0102288318
## Lag 4e+05 -0.0022485793
## Lag 5e+05 0.0096964639
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.001247232
## Lag 2e+05 -0.011077281
## Lag 3e+05 -0.004874879
## Lag 4e+05 0.043938182
## Lag 5e+05 0.012958581
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.009755835
## Lag 2e+05 0.022813442
## Lag 3e+05 -0.026204027
## Lag 4e+05 -0.022936040
## Lag 5e+05 0.005049265
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 -0.032813187 0.01130550
## Lag 2e+05 0.006394019 0.01336153
## Lag 3e+05 -0.026450550 -0.02075190
## Lag 4e+05 0.018984957 0.01599340
## Lag 5e+05 -0.010069015 -0.03595689
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.001475685 -0.0263703870 -0.045834960
## Lag 2e+05 -0.037526424 0.0006414004 -0.017826033
## Lag 3e+05 0.025882859 -0.0090626285 -0.020248780
## Lag 4e+05 -0.006698309 -0.0071757482 -0.015700931
## Lag 5e+05 -0.009709220 -0.0074316234 -0.008206175
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.011207233 -0.015918620
## Lag 2e+05 NaN -0.029379436 0.004717991
## Lag 3e+05 NaN 0.005213389 -0.013939879
## Lag 4e+05 NaN 0.015404411 -0.023393979
## Lag 5e+05 NaN 0.014494502 -0.028218686
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0127242045
## Lag 2e+05 0.0003179894
## Lag 3e+05 -0.0185138217
## Lag 4e+05 -0.0212854901
## Lag 5e+05 -0.0343258898
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.016570088 0.015945491
## Lag 2e+05 0.014360505 0.001957823
## Lag 3e+05 -0.030260908 -0.021616788
## Lag 4e+05 0.008793576 -0.016943128
## Lag 5e+05 -0.001246098 0.002499013
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 -0.005908801
## Lag 2e+05 -0.005642780
## Lag 3e+05 0.024266817
## Lag 4e+05 -0.006894492
## Lag 5e+05 0.004289095
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.001517593
## Lag 2e+05 -0.002616586
## Lag 3e+05 -0.006004276
## Lag 4e+05 -0.003205876
## Lag 5e+05 -0.006425879
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.019262737
## Lag 2e+05 -0.009717663
## Lag 3e+05 -0.020157961
## Lag 4e+05 -0.023209813
## Lag 5e+05 -0.005703401
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.014601731 0.003861248
## Lag 2e+05 0.005563967 0.002024204
## Lag 3e+05 0.010299065 -0.032901948
## Lag 4e+05 0.002860086 0.003664151
## Lag 5e+05 0.004961345 0.007383125
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.00000000 1.000000000 1.0000000000
## Lag 1e+05 -0.00324699 0.002696443 -0.0034260126
## Lag 2e+05 -0.01024943 0.018962006 -0.0002813096
## Lag 3e+05 -0.01506509 -0.016139609 -0.0245294187
## Lag 4e+05 0.02768955 -0.001003349 0.0110070450
## Lag 5e+05 -0.01710934 -0.035956990 -0.0045496472
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.0000000000
## Lag 1e+05 NaN 0.011714703 -0.0072573192
## Lag 2e+05 NaN -0.008738242 0.0032981640
## Lag 3e+05 NaN -0.005436846 -0.0101228962
## Lag 4e+05 NaN 0.019321869 0.0002283467
## Lag 5e+05 NaN 0.014063531 0.0036617345
## absdiff.sqrt.age
## Lag 0 1.00000000
## Lag 1e+05 -0.01961981
## Lag 2e+05 0.01574212
## Lag 3e+05 -0.01365908
## Lag 4e+05 0.00246615
## Lag 5e+05 -0.01317773
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.027214559 -0.016299436
## Lag 2e+05 -0.021741841 0.001471485
## Lag 3e+05 0.014918050 0.011430937
## Lag 4e+05 -0.006266827 0.027655925
## Lag 5e+05 -0.013643230 0.003657690
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.013000534
## Lag 2e+05 -0.013564773
## Lag 3e+05 -0.003317423
## Lag 4e+05 -0.002816633
## Lag 5e+05 -0.008108482
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.002923422
## Lag 2e+05 -0.006552494
## Lag 3e+05 0.025286439
## Lag 4e+05 0.005182189
## Lag 5e+05 0.001075252
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.033048374
## Lag 2e+05 -0.001446418
## Lag 3e+05 0.001886635
## Lag 4e+05 0.011924843
## Lag 5e+05 -0.017818677
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.002004997 -0.0009264303
## Lag 2e+05 -0.024401575 0.0115482931
## Lag 3e+05 0.030378981 0.0041296285
## Lag 4e+05 -0.015414178 0.0171065331
## Lag 5e+05 -0.002458135 0.0066072500
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.013458240 0.008928397 0.001228152
## Lag 2e+05 -0.022165628 0.015741361 0.014265919
## Lag 3e+05 -0.012385518 0.003600822 0.027370289
## Lag 4e+05 0.001907997 -0.004464104 -0.007145470
## Lag 5e+05 0.036073441 -0.004034306 0.011613905
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.010957694 -0.020134092
## Lag 2e+05 NaN -0.021769598 -0.024481115
## Lag 3e+05 NaN 0.005029376 0.020082841
## Lag 4e+05 NaN 0.022086437 -0.009264074
## Lag 5e+05 NaN -0.013009168 -0.023829157
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.004417282
## Lag 2e+05 -0.014015133
## Lag 3e+05 0.003266299
## Lag 4e+05 -0.002614453
## Lag 5e+05 -0.010438971
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.019780631 -0.001570657
## Lag 2e+05 0.016469165 -0.029966847
## Lag 3e+05 -0.022651521 0.002728267
## Lag 4e+05 0.001078758 0.017036899
## Lag 5e+05 0.024601159 -0.007310330
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000e+00
## Lag 1e+05 2.094967e-03
## Lag 2e+05 2.273921e-02
## Lag 3e+05 -7.698009e-05
## Lag 4e+05 1.048373e-03
## Lag 5e+05 -6.505166e-03
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0134056198
## Lag 2e+05 -0.0370667933
## Lag 3e+05 0.0008083579
## Lag 4e+05 -0.0058882589
## Lag 5e+05 0.0041203251
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.010295887
## Lag 2e+05 0.002661322
## Lag 3e+05 0.002000594
## Lag 4e+05 0.005341205
## Lag 5e+05 -0.007333352
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.008343426 0.010347760
## Lag 2e+05 -0.010871633 -0.003184313
## Lag 3e+05 0.026591831 0.006953221
## Lag 4e+05 -0.021068282 0.018230890
## Lag 5e+05 0.005842443 0.017916683
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 -0.001500148 0.0046034204 -0.010017978
## Lag 2e+05 -0.021456673 -0.0046246945 -0.003590417
## Lag 3e+05 -0.028586022 -0.0128619889 -0.018079315
## Lag 4e+05 -0.003830996 0.0029357498 0.009581578
## Lag 5e+05 0.012693663 0.0007158349 0.011783370
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN 0.025873051 -0.019946768
## Lag 2e+05 NaN -0.023348388 0.023800365
## Lag 3e+05 NaN -0.012113476 -0.008591662
## Lag 4e+05 NaN 0.006379161 -0.009732338
## Lag 5e+05 NaN 0.013105356 0.005112996
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 0.002827015
## Lag 2e+05 0.025214083
## Lag 3e+05 -0.031187960
## Lag 4e+05 0.015982239
## Lag 5e+05 0.003723767
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.002944805 -0.034033505
## Lag 2e+05 0.014668325 -0.009302330
## Lag 3e+05 0.014794588 0.001528488
## Lag 4e+05 0.005474308 -0.015706194
## Lag 5e+05 0.018940823 -0.004087485
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.002854076
## Lag 2e+05 -0.011935902
## Lag 3e+05 -0.017846142
## Lag 4e+05 0.008206171
## Lag 5e+05 -0.017991951
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0053036471
## Lag 2e+05 0.0222473725
## Lag 3e+05 -0.0081169843
## Lag 4e+05 0.0009750732
## Lag 5e+05 -0.0005359841
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.022235870
## Lag 2e+05 0.024111286
## Lag 3e+05 -0.014550622
## Lag 4e+05 -0.002278935
## Lag 5e+05 -0.001677464
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.0040941145 0.019906863
## Lag 2e+05 -0.0076752089 -0.032385654
## Lag 3e+05 -0.0092861156 -0.015088017
## Lag 4e+05 0.0062243455 0.001792484
## Lag 5e+05 0.0001165649 0.016404863
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0111806892 0.006029104 0.015270578
## Lag 2e+05 0.0002266331 -0.005595261 0.006745850
## Lag 3e+05 0.0077403678 0.009918769 0.006145884
## Lag 4e+05 0.0099564181 0.013915667 -0.020881549
## Lag 5e+05 -0.0267981464 0.032547415 0.001098759
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.005012424 0.016881636
## Lag 2e+05 NaN 0.047344958 0.011157016
## Lag 3e+05 NaN 0.012164671 0.003598625
## Lag 4e+05 NaN 0.006828074 -0.010545739
## Lag 5e+05 NaN -0.014594779 0.014376288
## absdiff.sqrt.age
## Lag 0 1.000000000
## Lag 1e+05 -0.017739504
## Lag 2e+05 -0.013718393
## Lag 3e+05 0.011196826
## Lag 4e+05 0.008692134
## Lag 5e+05 0.005013385
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 -0.014970724 0.005200106
## Lag 2e+05 0.013653682 -0.014489236
## Lag 3e+05 -0.027917714 -0.005196370
## Lag 4e+05 0.023148084 0.017432030
## Lag 5e+05 -0.004880321 -0.014370787
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0292102200
## Lag 2e+05 -0.0269437139
## Lag 3e+05 0.0096498356
## Lag 4e+05 0.0285146194
## Lag 5e+05 -0.0007426275
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.009659831
## Lag 2e+05 0.001713837
## Lag 3e+05 -0.002907522
## Lag 4e+05 0.033835251
## Lag 5e+05 -0.024026960
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 -0.018310956
## Lag 2e+05 0.002909554
## Lag 3e+05 -0.014039928
## Lag 4e+05 0.015339893
## Lag 5e+05 -0.005377928
## nodefactor.deg.main.deg.pers.1.2 nodefactor.race..wa.B
## Lag 0 1.000000000 1.000000e+00
## Lag 1e+05 -0.002779993 2.677834e-03
## Lag 2e+05 0.018298059 1.502947e-02
## Lag 3e+05 -0.005165912 -1.260458e-02
## Lag 4e+05 0.018797727 -7.672364e-05
## Lag 5e+05 0.016490648 -3.258503e-03
## nodefactor.race..wa.H nodefactor.region.EW nodefactor.region.OW
## Lag 0 1.000000000 1.00000000 1.000000000
## Lag 1e+05 -0.006101122 0.01155198 0.000251798
## Lag 2e+05 -0.028469288 0.01275553 0.011561489
## Lag 3e+05 -0.019608896 -0.02053795 -0.017322908
## Lag 4e+05 -0.025961959 0.01234252 0.002567985
## Lag 5e+05 -0.012568898 -0.01784316 0.013632498
## nodematch.race..wa.B nodematch.race..wa.H nodematch.race..wa.O
## Lag 0 NaN 1.000000000 1.000000000
## Lag 1e+05 NaN -0.003236059 -0.025513899
## Lag 2e+05 NaN 0.002812923 0.012069739
## Lag 3e+05 NaN -0.020704887 -0.006163815
## Lag 4e+05 NaN -0.047269104 0.003075766
## Lag 5e+05 NaN 0.048069886 0.024196928
## absdiff.sqrt.age
## Lag 0 1.0000000000
## Lag 1e+05 -0.0059556220
## Lag 2e+05 0.0076051272
## Lag 3e+05 -0.0090232706
## Lag 4e+05 0.0336374020
## Lag 5e+05 -0.0008586777
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.72378 -0.11626
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.09011 -0.67347
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.69219 -0.24630
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.29340 0.52136
## nodefactor.region.EW nodefactor.region.OW
## 0.80635 0.62426
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.82516
## nodematch.race..wa.O absdiff.sqrt.age
## 0.32264 0.60639
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4692007 0.9074469
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.9282031 0.5006509
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4888156 0.8054481
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.7692193 0.6021130
## nodefactor.region.EW nodefactor.region.OW
## 0.4200434 0.5324550
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.4092793
## nodematch.race..wa.O absdiff.sqrt.age
## 0.7469681 0.5442531
## Joint P-value (lower = worse): 0.9744758 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.32807 0.67497
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.35634 -1.52446
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.09233 -0.45062
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.14301 1.23381
## nodefactor.region.EW nodefactor.region.OW
## 0.13237 -0.61665
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.04806
## nodematch.race..wa.O absdiff.sqrt.age
## -1.15238 -1.01498
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7428608 0.4996954
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1749912 0.1273947
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.9264374 0.6522655
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.8862842 0.2172735
## nodefactor.region.EW nodefactor.region.OW
## 0.8946911 0.5374663
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.9616703
## nodematch.race..wa.O absdiff.sqrt.age
## 0.2491665 0.3101168
## Joint P-value (lower = worse): 0.7277695 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.02761 1.32866
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.20200 -1.12687
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.33890 -0.49716
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.18312 -0.47284
## nodefactor.region.EW nodefactor.region.OW
## -0.79236 0.48598
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -1.20431
## nodematch.race..wa.O absdiff.sqrt.age
## -0.54239 0.67171
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.9779753 0.1839585
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.8399179 0.2597964
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7346874 0.6190772
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.2367597 0.6363302
## nodefactor.region.EW nodefactor.region.OW
## 0.4281516 0.6269832
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.2284679
## nodematch.race..wa.O absdiff.sqrt.age
## 0.5875510 0.5017652
## Joint P-value (lower = worse): 0.852875 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7533 0.4672
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1330 1.0433
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.5375 0.3701
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.3134 -0.3495
## nodefactor.region.EW nodefactor.region.OW
## -2.4509 0.3408
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.2091
## nodematch.race..wa.O absdiff.sqrt.age
## 1.0046 1.4842
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.45125794 0.64033339
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.89415804 0.29682336
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.59089274 0.71127251
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.75399822 0.72674246
## nodefactor.region.EW nodefactor.region.OW
## 0.01424804 0.73326850
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.83438877
## nodematch.race..wa.O absdiff.sqrt.age
## 0.31506754 0.13774303
## Joint P-value (lower = worse): 0.5030046 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.82133 0.35860
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.05003 0.18208
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.34493 0.89177
## nodefactor.race..wa.B nodefactor.race..wa.H
## -0.64871 0.15472
## nodefactor.region.EW nodefactor.region.OW
## 0.38242 -0.63965
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.37437
## nodematch.race..wa.O absdiff.sqrt.age
## 1.31588 -0.05213
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.4114597 0.7198928
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.9600974 0.8555197
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7301477 0.3725165
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.5165259 0.8770436
## nodefactor.region.EW nodefactor.region.OW
## 0.7021472 0.5224014
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.7081326
## nodematch.race..wa.O absdiff.sqrt.age
## 0.1882154 0.9584237
## Joint P-value (lower = worse): 0.9211648 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.85085 -0.38745
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.61558 1.60687
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.70918 1.23653
## nodefactor.race..wa.B nodefactor.race..wa.H
## 1.40982 0.41193
## nodefactor.region.EW nodefactor.region.OW
## 0.72537 -0.03523
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -1.41312
## nodematch.race..wa.O absdiff.sqrt.age
## -0.06633 0.85788
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.3948522 0.6984216
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.5381744 0.1080830
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4782104 0.2162605
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.1585936 0.6803932
## nodefactor.region.EW nodefactor.region.OW
## 0.4682245 0.9719003
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.1576208
## nodematch.race..wa.O absdiff.sqrt.age
## 0.9471147 0.3909578
## Joint P-value (lower = worse): 0.6706843 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.3153 0.1738
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.5981 0.6326
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.7857 1.7644
## nodefactor.race..wa.B nodefactor.race..wa.H
## -2.0396 -0.3636
## nodefactor.region.EW nodefactor.region.OW
## -1.0007 -0.7458
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 1.1230
## nodematch.race..wa.O absdiff.sqrt.age
## 1.0895 -0.4911
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.75251933 0.86205854
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.11001215 0.52699791
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.43202067 0.07766762
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.04138605 0.71612360
## nodefactor.region.EW nodefactor.region.OW
## 0.31696941 0.45576287
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.26143342
## nodematch.race..wa.O absdiff.sqrt.age
## 0.27592923 0.62334366
## Joint P-value (lower = worse): 0.1881114 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.06097 1.00028
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.89386 -0.61363
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.09069 0.12623
## nodefactor.race..wa.B nodefactor.race..wa.H
## -1.72746 -0.12068
## nodefactor.region.EW nodefactor.region.OW
## 1.28776 1.18485
## nodematch.race..wa.B nodematch.race..wa.H
## NaN -0.74948
## nodematch.race..wa.O absdiff.sqrt.age
## 0.76758 -0.15436
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.95138596 0.31717573
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.37139713 0.53945991
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.92773836 0.89955372
## nodefactor.race..wa.B nodefactor.race..wa.H
## 0.08408497 0.90394606
## nodefactor.region.EW nodefactor.region.OW
## 0.19782832 0.23607743
## nodematch.race..wa.B nodematch.race..wa.H
## NaN 0.45356593
## nodematch.race..wa.O absdiff.sqrt.age
## 0.44273884 0.87732407
## Joint P-value (lower = worse): 0.5679515 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 7
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -2.78043 21.924 0.12658 0.14990
## nodefactor.deg.main.deg.pers.0.1 0.57943 14.280 0.08245 0.11037
## nodefactor.deg.main.deg.pers.0.2 0.02853 6.256 0.03612 0.03653
## nodefactor.deg.main.deg.pers.1.0 0.06819 6.160 0.03557 0.03557
## nodefactor.deg.main.deg.pers.1.1 0.11538 12.395 0.07156 0.09681
## nodefactor.deg.main.deg.pers.1.2 0.35236 13.016 0.07515 0.09403
## nodefactor.riskg.O2 -0.40092 0.000 0.00000 0.00000
## nodefactor.riskg.O3 -1.44772 2.341 0.01352 0.01348
## nodefactor.riskg.O4 -1.43580 11.546 0.06666 0.06963
## nodefactor.riskg.Y1 -1.34908 0.000 0.00000 0.00000
## nodefactor.riskg.Y2 -1.49884 2.609 0.01506 0.01501
## nodefactor.riskg.Y3 -1.44773 8.571 0.04948 0.04941
## nodefactor.riskg.Y4 -0.07479 37.320 0.21547 0.26598
## nodefactor.race..wa.B 6.53157 9.080 0.05242 0.06894
## nodefactor.race..wa.H 6.38132 11.003 0.06353 0.07767
## nodefactor.region.EW -0.04259 9.554 0.05516 0.06126
## nodefactor.region.OW 0.80288 17.351 0.10018 0.10658
## nodematch.race..wa.B -2.53754 0.000 0.00000 0.00000
## nodematch.race..wa.H -6.38123 2.628 0.01518 0.01958
## nodematch.race..wa.O 6.80672 17.220 0.09942 0.11307
## absdiff.sqrt.age 0.25493 22.438 0.12955 0.13870
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75%
## edges -45.5122 -17.5122 -2.51216 11.4878
## nodefactor.deg.main.deg.pers.0.1 -27.1754 -9.1754 0.82459 9.8246
## nodefactor.deg.main.deg.pers.0.2 -11.2419 -4.2169 -0.21687 3.7831
## nodefactor.deg.main.deg.pers.1.0 -11.5677 -4.5677 -0.56768 4.4323
## nodefactor.deg.main.deg.pers.1.1 -23.3643 -8.3643 -0.36432 8.6357
## nodefactor.deg.main.deg.pers.1.2 -24.8703 -8.8703 0.12966 9.1297
## nodefactor.riskg.O2 -0.4009 -0.4009 -0.40092 -0.4009
## nodefactor.riskg.O3 -5.8558 -2.8558 -1.85582 0.1442
## nodefactor.riskg.O4 -23.5127 -9.5127 -1.51266 6.4873
## nodefactor.riskg.Y1 -1.3491 -1.3491 -1.34908 -1.3491
## nodefactor.riskg.Y2 -6.2024 -3.2024 -1.20238 -0.2024
## nodefactor.riskg.Y3 -17.7860 -7.7860 -1.78597 4.2140
## nodefactor.riskg.Y4 -73.0115 -25.0115 -0.01152 24.9885
## nodefactor.race..wa.B -11.1865 -0.1865 6.81350 12.8135
## nodefactor.race..wa.H -14.8353 -0.8353 6.16472 14.1647
## nodefactor.region.EW -18.3887 -6.3887 -0.38872 6.6113
## nodefactor.region.OW -32.1591 -11.1591 0.84094 11.8409
## nodematch.race..wa.B -2.5375 -2.5375 -2.53754 -2.5375
## nodematch.race..wa.H -11.2750 -8.2750 -6.27496 -4.2750
## nodematch.race..wa.O -26.8841 -4.8841 7.11592 18.1159
## absdiff.sqrt.age -42.9408 -14.8934 -0.06949 15.3776
## 97.5%
## edges 40.4878
## nodefactor.deg.main.deg.pers.0.1 28.8246
## nodefactor.deg.main.deg.pers.0.2 12.7831
## nodefactor.deg.main.deg.pers.1.0 12.4323
## nodefactor.deg.main.deg.pers.1.1 25.6357
## nodefactor.deg.main.deg.pers.1.2 26.1297
## nodefactor.riskg.O2 -0.4009
## nodefactor.riskg.O3 3.1442
## nodefactor.riskg.O4 21.4873
## nodefactor.riskg.Y1 -1.3491
## nodefactor.riskg.Y2 3.7976
## nodefactor.riskg.Y3 16.2140
## nodefactor.riskg.Y4 72.9885
## nodefactor.race..wa.B 24.8135
## nodefactor.race..wa.H 28.1647
## nodefactor.region.EW 19.6113
## nodefactor.region.OW 35.8409
## nodematch.race..wa.B -2.5375
## nodematch.race..wa.H -1.2750
## nodematch.race..wa.O 41.1159
## absdiff.sqrt.age 45.0626
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5605254
## nodefactor.deg.main.deg.pers.0.2 0.2739066
## nodefactor.deg.main.deg.pers.1.0 0.2740702
## nodefactor.deg.main.deg.pers.1.1 0.4970060
## nodefactor.deg.main.deg.pers.1.2 0.5197052
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.1007890
## nodefactor.riskg.O4 0.4431162
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.1301309
## nodefactor.riskg.Y3 0.3601217
## nodefactor.riskg.Y4 0.9396998
## nodefactor.race..wa.B 0.4117198
## nodefactor.race..wa.H 0.4498201
## nodefactor.region.EW 0.4049308
## nodefactor.region.OW 0.6340514
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.1219231
## nodematch.race..wa.O 0.7872474
## absdiff.sqrt.age 0.7777615
## nodefactor.deg.main.deg.pers.0.1
## edges 0.56052539
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.07454453
## nodefactor.deg.main.deg.pers.1.0 0.08588406
## nodefactor.deg.main.deg.pers.1.1 0.13951861
## nodefactor.deg.main.deg.pers.1.2 0.14556992
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.05660025
## nodefactor.riskg.O4 0.25685686
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.06280708
## nodefactor.riskg.Y3 0.19486200
## nodefactor.riskg.Y4 0.52641072
## nodefactor.race..wa.B 0.22523125
## nodefactor.race..wa.H 0.20850976
## nodefactor.region.EW 0.21549292
## nodefactor.region.OW 0.34369663
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.05045618
## nodematch.race..wa.O 0.46934321
## absdiff.sqrt.age 0.44714061
## nodefactor.deg.main.deg.pers.0.2
## edges 0.27390664
## nodefactor.deg.main.deg.pers.0.1 0.07454453
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03129919
## nodefactor.deg.main.deg.pers.1.1 0.07278331
## nodefactor.deg.main.deg.pers.1.2 0.07141051
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.04230945
## nodefactor.riskg.O4 0.10481873
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.03173576
## nodefactor.riskg.Y3 0.09184978
## nodefactor.riskg.Y4 0.26342180
## nodefactor.race..wa.B 0.10940619
## nodefactor.race..wa.H 0.11784089
## nodefactor.region.EW 0.10227189
## nodefactor.region.OW 0.18720337
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03540397
## nodematch.race..wa.O 0.22114384
## absdiff.sqrt.age 0.21524177
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27407021
## nodefactor.deg.main.deg.pers.0.1 0.08588406
## nodefactor.deg.main.deg.pers.0.2 0.03129919
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.06600732
## nodefactor.deg.main.deg.pers.1.2 0.06978487
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.02584123
## nodefactor.riskg.O4 0.13229956
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.04362079
## nodefactor.riskg.Y3 0.09964898
## nodefactor.riskg.Y4 0.25352273
## nodefactor.race..wa.B 0.09673413
## nodefactor.race..wa.H 0.13340136
## nodefactor.region.EW 0.11971967
## nodefactor.region.OW 0.15902807
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.02950182
## nodematch.race..wa.O 0.21719009
## absdiff.sqrt.age 0.21741229
## nodefactor.deg.main.deg.pers.1.1
## edges 0.49700599
## nodefactor.deg.main.deg.pers.0.1 0.13951861
## nodefactor.deg.main.deg.pers.0.2 0.07278331
## nodefactor.deg.main.deg.pers.1.0 0.06600732
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13017730
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.05598420
## nodefactor.riskg.O4 0.23702949
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.07362575
## nodefactor.riskg.Y3 0.19148955
## nodefactor.riskg.Y4 0.45797171
## nodefactor.race..wa.B 0.16723786
## nodefactor.race..wa.H 0.22908921
## nodefactor.region.EW 0.14899367
## nodefactor.region.OW 0.28156331
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.06350756
## nodematch.race..wa.O 0.40789437
## absdiff.sqrt.age 0.39219056
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51970522
## nodefactor.deg.main.deg.pers.0.1 0.14556992
## nodefactor.deg.main.deg.pers.0.2 0.07141051
## nodefactor.deg.main.deg.pers.1.0 0.06978487
## nodefactor.deg.main.deg.pers.1.1 0.13017730
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.04908002
## nodefactor.riskg.O4 0.22980741
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.05779263
## nodefactor.riskg.Y3 0.18419879
## nodefactor.riskg.Y4 0.49009034
## nodefactor.race..wa.B 0.19340128
## nodefactor.race..wa.H 0.27115908
## nodefactor.region.EW 0.24797115
## nodefactor.region.OW 0.33426849
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.08216250
## nodematch.race..wa.O 0.39896472
## absdiff.sqrt.age 0.39361530
## nodefactor.riskg.O2 nodefactor.riskg.O3
## edges NA 0.100789045
## nodefactor.deg.main.deg.pers.0.1 NA 0.056600247
## nodefactor.deg.main.deg.pers.0.2 NA 0.042309452
## nodefactor.deg.main.deg.pers.1.0 NA 0.025841225
## nodefactor.deg.main.deg.pers.1.1 NA 0.055984200
## nodefactor.deg.main.deg.pers.1.2 NA 0.049080019
## nodefactor.riskg.O2 1 NA
## nodefactor.riskg.O3 NA 1.000000000
## nodefactor.riskg.O4 NA 0.048733107
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA 0.005793377
## nodefactor.riskg.Y3 NA 0.020443779
## nodefactor.riskg.Y4 NA 0.035508134
## nodefactor.race..wa.B NA 0.036353605
## nodefactor.race..wa.H NA 0.044670436
## nodefactor.region.EW NA 0.047443109
## nodefactor.region.OW NA 0.065192544
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H NA 0.009761303
## nodematch.race..wa.O NA 0.082097910
## absdiff.sqrt.age NA 0.101819078
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges 0.44311618 NA
## nodefactor.deg.main.deg.pers.0.1 0.25685686 NA
## nodefactor.deg.main.deg.pers.0.2 0.10481873 NA
## nodefactor.deg.main.deg.pers.1.0 0.13229956 NA
## nodefactor.deg.main.deg.pers.1.1 0.23702949 NA
## nodefactor.deg.main.deg.pers.1.2 0.22980741 NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.04873311 NA
## nodefactor.riskg.O4 1.00000000 NA
## nodefactor.riskg.Y1 NA 1
## nodefactor.riskg.Y2 0.02647222 NA
## nodefactor.riskg.Y3 0.07434586 NA
## nodefactor.riskg.Y4 0.18925336 NA
## nodefactor.race..wa.B 0.16702725 NA
## nodefactor.race..wa.H 0.16340843 NA
## nodefactor.region.EW 0.18825714 NA
## nodefactor.region.OW 0.27393940 NA
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.03794673 NA
## nodematch.race..wa.O 0.37746230 NA
## absdiff.sqrt.age 0.43607921 NA
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges 0.130130855 0.36012174
## nodefactor.deg.main.deg.pers.0.1 0.062807082 0.19486200
## nodefactor.deg.main.deg.pers.0.2 0.031735756 0.09184978
## nodefactor.deg.main.deg.pers.1.0 0.043620786 0.09964898
## nodefactor.deg.main.deg.pers.1.1 0.073625746 0.19148955
## nodefactor.deg.main.deg.pers.1.2 0.057792629 0.18419879
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.005793377 0.02044378
## nodefactor.riskg.O4 0.026472218 0.07434586
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 1.000000000 0.02380483
## nodefactor.riskg.Y3 0.023804828 1.00000000
## nodefactor.riskg.Y4 0.068959172 0.16751269
## nodefactor.race..wa.B 0.059980442 0.14184952
## nodefactor.race..wa.H 0.048167781 0.18255512
## nodefactor.region.EW 0.049491417 0.14858426
## nodefactor.region.OW 0.084386499 0.23215072
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.014376806 0.05370028
## nodematch.race..wa.O 0.105466428 0.27524416
## absdiff.sqrt.age 0.102258333 0.27654786
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges 0.93969982 0.4117198470
## nodefactor.deg.main.deg.pers.0.1 0.52641072 0.2252312544
## nodefactor.deg.main.deg.pers.0.2 0.26342180 0.1094061947
## nodefactor.deg.main.deg.pers.1.0 0.25352273 0.0967341318
## nodefactor.deg.main.deg.pers.1.1 0.45797171 0.1672378572
## nodefactor.deg.main.deg.pers.1.2 0.49009034 0.1934012770
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.03550813 0.0363536050
## nodefactor.riskg.O4 0.18925336 0.1670272532
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 0.06895917 0.0599804424
## nodefactor.riskg.Y3 0.16751269 0.1418495154
## nodefactor.riskg.Y4 1.00000000 0.3930108559
## nodefactor.race..wa.B 0.39301086 1.0000000000
## nodefactor.race..wa.H 0.42985122 -0.0008758497
## nodefactor.region.EW 0.37695689 0.1011031305
## nodefactor.region.OW 0.59690150 0.2474581662
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.11755962 -0.0025292097
## nodematch.race..wa.O 0.73243463 -0.0029159437
## absdiff.sqrt.age 0.70184169 0.3190775130
## nodefactor.race..wa.H
## edges 0.4498201209
## nodefactor.deg.main.deg.pers.0.1 0.2085097615
## nodefactor.deg.main.deg.pers.0.2 0.1178408947
## nodefactor.deg.main.deg.pers.1.0 0.1334013569
## nodefactor.deg.main.deg.pers.1.1 0.2290892071
## nodefactor.deg.main.deg.pers.1.2 0.2711590810
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.0446704356
## nodefactor.riskg.O4 0.1634084264
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.0481677806
## nodefactor.riskg.Y3 0.1825551171
## nodefactor.riskg.Y4 0.4298512178
## nodefactor.race..wa.B -0.0008758497
## nodefactor.race..wa.H 1.0000000000
## nodefactor.region.EW 0.2847026380
## nodefactor.region.OW 0.2982866880
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.4771378032
## nodematch.race..wa.O 0.0070082673
## absdiff.sqrt.age 0.3424946071
## nodefactor.region.EW nodefactor.region.OW
## edges 0.40493077 0.63405139
## nodefactor.deg.main.deg.pers.0.1 0.21549292 0.34369663
## nodefactor.deg.main.deg.pers.0.2 0.10227189 0.18720337
## nodefactor.deg.main.deg.pers.1.0 0.11971967 0.15902807
## nodefactor.deg.main.deg.pers.1.1 0.14899367 0.28156331
## nodefactor.deg.main.deg.pers.1.2 0.24797115 0.33426849
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.04744311 0.06519254
## nodefactor.riskg.O4 0.18825714 0.27393940
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 0.04949142 0.08438650
## nodefactor.riskg.Y3 0.14858426 0.23215072
## nodefactor.riskg.Y4 0.37695689 0.59690150
## nodefactor.race..wa.B 0.10110313 0.24745817
## nodefactor.race..wa.H 0.28470264 0.29828669
## nodefactor.region.EW 1.00000000 0.13377556
## nodefactor.region.OW 0.13377556 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.10136292 0.08093731
## nodematch.race..wa.O 0.29578286 0.49852081
## absdiff.sqrt.age 0.31029802 0.49877732
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.121923149
## nodefactor.deg.main.deg.pers.0.1 NA 0.050456179
## nodefactor.deg.main.deg.pers.0.2 NA 0.035403966
## nodefactor.deg.main.deg.pers.1.0 NA 0.029501818
## nodefactor.deg.main.deg.pers.1.1 NA 0.063507562
## nodefactor.deg.main.deg.pers.1.2 NA 0.082162499
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA 0.009761303
## nodefactor.riskg.O4 NA 0.037946733
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA 0.014376806
## nodefactor.riskg.Y3 NA 0.053700281
## nodefactor.riskg.Y4 NA 0.117559617
## nodefactor.race..wa.B NA -0.002529210
## nodefactor.race..wa.H NA 0.477137803
## nodefactor.region.EW NA 0.101362915
## nodefactor.region.OW NA 0.080937309
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA 0.004322449
## absdiff.sqrt.age NA 0.087913161
## nodematch.race..wa.O absdiff.sqrt.age
## edges 0.787247394 0.77776154
## nodefactor.deg.main.deg.pers.0.1 0.469343210 0.44714061
## nodefactor.deg.main.deg.pers.0.2 0.221143843 0.21524177
## nodefactor.deg.main.deg.pers.1.0 0.217190090 0.21741229
## nodefactor.deg.main.deg.pers.1.1 0.407894370 0.39219056
## nodefactor.deg.main.deg.pers.1.2 0.398964724 0.39361530
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.082097910 0.10181908
## nodefactor.riskg.O4 0.377462296 0.43607921
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 0.105466428 0.10225833
## nodefactor.riskg.Y3 0.275244157 0.27654786
## nodefactor.riskg.Y4 0.732434632 0.70184169
## nodefactor.race..wa.B -0.002915944 0.31907751
## nodefactor.race..wa.H 0.007008267 0.34249461
## nodefactor.region.EW 0.295782863 0.31029802
## nodefactor.region.OW 0.498520806 0.49877732
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.004322449 0.08791316
## nodematch.race..wa.O 1.000000000 0.61653938
## absdiff.sqrt.age 0.616539378 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.13468509 0.23808224
## Lag 2e+05 0.06179420 0.11862717
## Lag 3e+05 0.04815151 0.05756046
## Lag 4e+05 0.02969748 0.04370035
## Lag 5e+05 0.02661905 0.01389898
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.015096844
## Lag 2e+05 0.021169862
## Lag 3e+05 -0.004870052
## Lag 4e+05 0.032936814
## Lag 5e+05 0.002277464
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003856845
## Lag 2e+05 -0.019514635
## Lag 3e+05 0.010967055
## Lag 4e+05 -0.018721342
## Lag 5e+05 -0.023307730
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.25017721
## Lag 2e+05 0.14068745
## Lag 3e+05 0.08702326
## Lag 4e+05 0.04404667
## Lag 5e+05 0.01913111
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.18732751 NaN
## Lag 2e+05 0.05698054 NaN
## Lag 3e+05 0.01403623 NaN
## Lag 4e+05 0.02084029 NaN
## Lag 5e+05 0.01506449 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 0.030897518 0.041777794 NaN
## Lag 2e+05 -0.001292263 0.027269941 NaN
## Lag 3e+05 0.011655779 0.019210357 NaN
## Lag 4e+05 -0.007796472 -0.030315018 NaN
## Lag 5e+05 0.023331975 0.006777293 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.01967175 0.014199622 0.17320150
## Lag 2e+05 0.01210264 -0.010027096 0.08496247
## Lag 3e+05 0.00436030 0.002427261 0.05083311
## Lag 4e+05 0.00312743 0.011437854 0.04220305
## Lag 5e+05 -0.02187181 -0.002679297 0.03447290
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.18883003 0.157859893 0.139188321
## Lag 2e+05 0.08374807 0.058160208 0.014042578
## Lag 3e+05 0.07271627 0.045407341 -0.037193306
## Lag 4e+05 0.06176667 0.018787726 0.001957531
## Lag 5e+05 0.01071464 -0.006124526 0.006344720
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.064542272 NaN 0.18591604
## Lag 2e+05 0.017079930 NaN 0.11044324
## Lag 3e+05 0.006898274 NaN 0.08176853
## Lag 4e+05 0.019434732 NaN 0.03210445
## Lag 5e+05 -0.003484503 NaN 0.02573763
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.10559182 0.061357354
## Lag 2e+05 0.02688983 0.035338247
## Lag 3e+05 0.05394412 -0.000495449
## Lag 4e+05 0.02036580 0.011207598
## Lag 5e+05 0.01579862 0.021349941
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.09851394 0.21853318
## Lag 2e+05 0.00465723 0.09021363
## Lag 3e+05 0.02901316 0.07309348
## Lag 4e+05 0.02634481 0.06132681
## Lag 5e+05 -0.01836916 0.01615536
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 -0.0001280749
## Lag 2e+05 -0.0130842366
## Lag 3e+05 0.0083251224
## Lag 4e+05 -0.0234647178
## Lag 5e+05 0.0109897327
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0022883946
## Lag 2e+05 0.0045646716
## Lag 3e+05 -0.0158709546
## Lag 4e+05 -0.0082576830
## Lag 5e+05 0.0001882364
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.211011461
## Lag 2e+05 0.079612254
## Lag 3e+05 0.043045546
## Lag 4e+05 0.026893036
## Lag 5e+05 0.002733771
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.16915425 NaN
## Lag 2e+05 0.04932373 NaN
## Lag 3e+05 0.02901708 NaN
## Lag 4e+05 0.03255304 NaN
## Lag 5e+05 -0.01108038 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.0000000000 NaN
## Lag 1e+05 0.022145616 0.0419144789 NaN
## Lag 2e+05 -0.006458181 -0.0045295890 NaN
## Lag 3e+05 0.003662739 0.0075451858 NaN
## Lag 4e+05 0.006207209 0.0001433991 NaN
## Lag 5e+05 -0.013992339 -0.0198887617 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.022569499 0.012458794 0.12652886
## Lag 2e+05 -0.009981778 -0.009569254 0.02481685
## Lag 3e+05 -0.026933152 0.038891371 0.02490411
## Lag 4e+05 -0.007014203 -0.005074752 0.02248224
## Lag 5e+05 0.023220825 -0.001484974 -0.01870880
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.20033710 0.12405030 0.114127636
## Lag 2e+05 0.08713704 0.05842359 0.018665401
## Lag 3e+05 0.05525040 0.03748294 -0.015773112
## Lag 4e+05 0.03656093 0.03107247 -0.019058025
## Lag 5e+05 0.01537730 0.02206032 -0.008724877
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.029522128 NaN 0.16400749
## Lag 2e+05 0.008307655 NaN 0.05451279
## Lag 3e+05 0.028920537 NaN 0.01066813
## Lag 4e+05 0.005454676 NaN 0.02906446
## Lag 5e+05 -0.016745217 NaN 0.01012355
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.095052235 0.030373828
## Lag 2e+05 -0.004965831 -0.016397981
## Lag 3e+05 0.025622786 0.008558745
## Lag 4e+05 0.024584956 0.011814286
## Lag 5e+05 -0.036319362 -0.013394557
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.0000000000
## Lag 1e+05 0.110850884 0.2045724509
## Lag 2e+05 0.035746543 0.0759081402
## Lag 3e+05 0.003551163 0.0376044322
## Lag 4e+05 -0.019560883 0.0009708908
## Lag 5e+05 0.018305229 0.0037092706
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.052868654
## Lag 2e+05 0.003509665
## Lag 3e+05 0.003520507
## Lag 4e+05 -0.012880474
## Lag 5e+05 0.005844861
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.022456189
## Lag 2e+05 0.008569368
## Lag 3e+05 0.008446430
## Lag 4e+05 -0.030634198
## Lag 5e+05 0.021013016
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.000000000
## Lag 1e+05 0.236689961
## Lag 2e+05 0.071673068
## Lag 3e+05 0.044605931
## Lag 4e+05 0.029816792
## Lag 5e+05 0.009577031
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.000000000 NaN
## Lag 1e+05 0.175382717 NaN
## Lag 2e+05 0.092716784 NaN
## Lag 3e+05 0.047509727 NaN
## Lag 4e+05 -0.005429058 NaN
## Lag 5e+05 -0.018316763 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.00000000 NaN
## Lag 1e+05 0.034360591 0.02779117 NaN
## Lag 2e+05 0.003654276 0.01453083 NaN
## Lag 3e+05 -0.019856003 -0.01070607 NaN
## Lag 4e+05 0.007431766 -0.02703800 NaN
## Lag 5e+05 -0.003801274 0.03091276 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0121716735 -0.008946987 0.127859282
## Lag 2e+05 0.0306529970 -0.000548480 0.023346211
## Lag 3e+05 -0.0001698867 0.010112117 0.003778167
## Lag 4e+05 0.0226946378 -0.013097869 0.003143204
## Lag 5e+05 -0.0126464931 -0.002681296 0.010938883
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.19360889 0.17742074 0.11861781
## Lag 2e+05 0.05501734 0.06896667 0.03432955
## Lag 3e+05 0.04700043 0.01051782 -0.03522659
## Lag 4e+05 0.02781616 -0.01116838 0.01106236
## Lag 5e+05 0.01646321 -0.01233376 0.01392155
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.00000000
## Lag 1e+05 0.0534262624 NaN 0.19026381
## Lag 2e+05 -0.0002709374 NaN 0.09187678
## Lag 3e+05 0.0038533056 NaN 0.05791957
## Lag 4e+05 -0.0376999340 NaN 0.02293692
## Lag 5e+05 -0.0072821303 NaN 0.03594087
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.090105325 0.034708958
## Lag 2e+05 0.026797289 0.006300416
## Lag 3e+05 0.009574458 0.008682453
## Lag 4e+05 -0.006867847 -0.043148882
## Lag 5e+05 0.016775531 0.023547176
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.09290302 0.22096252
## Lag 2e+05 0.08370338 0.14469869
## Lag 3e+05 0.01409275 0.05747672
## Lag 4e+05 0.01045947 0.01430869
## Lag 5e+05 0.01223927 0.03403705
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.022036970
## Lag 2e+05 0.001034541
## Lag 3e+05 -0.023540139
## Lag 4e+05 -0.001068858
## Lag 5e+05 0.003695188
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 -0.0135013331
## Lag 2e+05 -0.0038894548
## Lag 3e+05 0.0299238234
## Lag 4e+05 -0.0057522120
## Lag 5e+05 -0.0007255634
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.22692099
## Lag 2e+05 0.12767433
## Lag 3e+05 0.05053357
## Lag 4e+05 0.03511662
## Lag 5e+05 0.02597186
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.21370478 NaN
## Lag 2e+05 0.09377703 NaN
## Lag 3e+05 0.06226017 NaN
## Lag 4e+05 0.04096922 NaN
## Lag 5e+05 0.02780972 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000e+00 1.00000000 NaN
## Lag 1e+05 -2.296505e-02 0.04452961 NaN
## Lag 2e+05 8.674597e-06 0.02950926 NaN
## Lag 3e+05 -1.426885e-02 0.01237768 NaN
## Lag 4e+05 5.343195e-03 0.00754383 NaN
## Lag 5e+05 -9.326690e-03 0.00455378 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.007417531 0.006562911 0.13795876
## Lag 2e+05 0.003322203 -0.016154668 0.08754619
## Lag 3e+05 0.014340663 -0.036365082 0.01336508
## Lag 4e+05 -0.005843071 -0.007413842 0.01985218
## Lag 5e+05 -0.006555389 0.003993605 0.02127420
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.16277034 0.12330704 0.09469469
## Lag 2e+05 0.09199799 0.05124610 0.04193576
## Lag 3e+05 0.02830096 0.01620112 0.01188139
## Lag 4e+05 0.02024980 0.02805925 -0.01004093
## Lag 5e+05 0.01575226 0.01114279 -0.01454849
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.0000000000 NaN 1.00000000
## Lag 1e+05 0.0419516743 NaN 0.18552813
## Lag 2e+05 0.0407468114 NaN 0.10444687
## Lag 3e+05 0.0154689215 NaN 0.03140037
## Lag 4e+05 0.0108476879 NaN 0.03183470
## Lag 5e+05 -0.0004602954 NaN 0.01417561
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.108550643 0.039794851
## Lag 2e+05 0.059205557 0.033736619
## Lag 3e+05 0.032402334 0.007711898
## Lag 4e+05 -0.002042805 0.011647397
## Lag 5e+05 0.011185263 0.002771941
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.130721798 0.233413356
## Lag 2e+05 0.017395593 0.098349749
## Lag 3e+05 -0.003285385 0.049964824
## Lag 4e+05 0.006445234 0.020593925
## Lag 5e+05 0.013881532 0.009026563
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.034494072
## Lag 2e+05 -0.011047010
## Lag 3e+05 -0.002904326
## Lag 4e+05 0.022721800
## Lag 5e+05 0.010460012
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.008620757
## Lag 2e+05 0.005918871
## Lag 3e+05 -0.020454197
## Lag 4e+05 0.024448376
## Lag 5e+05 -0.011980332
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.24977292
## Lag 2e+05 0.09874657
## Lag 3e+05 0.04642508
## Lag 4e+05 0.04689410
## Lag 5e+05 0.03041982
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.000000000 NaN
## Lag 1e+05 0.197186335 NaN
## Lag 2e+05 0.045925713 NaN
## Lag 3e+05 -0.002232392 NaN
## Lag 4e+05 0.012653506 NaN
## Lag 5e+05 0.037688541 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.0000000000 NaN
## Lag 1e+05 -0.036218711 0.0502643096 NaN
## Lag 2e+05 -0.014938569 -0.0077994297 NaN
## Lag 3e+05 -0.016813726 -0.0112128636 NaN
## Lag 4e+05 0.004786666 0.0002880085 NaN
## Lag 5e+05 0.026454991 -0.0072216605 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 0.0081167992 -0.004542726 0.163637038
## Lag 2e+05 -0.0181545950 -0.021500628 0.033449098
## Lag 3e+05 -0.0005634124 -0.006186686 0.006117211
## Lag 4e+05 0.0147676630 0.009414542 0.007547344
## Lag 5e+05 0.0108830199 0.008333332 0.012164163
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.202076327 0.151422830 0.067799672
## Lag 2e+05 0.088058210 0.062093049 0.026084887
## Lag 3e+05 0.054610626 -0.002354675 0.001894431
## Lag 4e+05 0.045577992 -0.005169030 -0.031561689
## Lag 5e+05 0.007259537 0.006352411 0.005808390
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.000000000
## Lag 1e+05 0.063983032 NaN 0.187134725
## Lag 2e+05 0.020842226 NaN 0.065161811
## Lag 3e+05 0.009544928 NaN 0.031838462
## Lag 4e+05 0.018137053 NaN 0.004461249
## Lag 5e+05 -0.005467869 NaN -0.001010826
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.0000000000
## Lag 1e+05 0.10849902 0.0523994498
## Lag 2e+05 0.03333136 0.0009634913
## Lag 3e+05 0.01856035 -0.0029688872
## Lag 4e+05 0.01044798 -0.0107429037
## Lag 5e+05 0.00989780 -0.0044432619
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.14069240 0.25410382
## Lag 2e+05 0.05753907 0.08951555
## Lag 3e+05 0.01409801 0.04707362
## Lag 4e+05 -0.01511485 0.01836147
## Lag 5e+05 -0.03317312 0.01361313
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.018853476
## Lag 2e+05 -0.012706376
## Lag 3e+05 -0.019656284
## Lag 4e+05 -0.001300013
## Lag 5e+05 -0.013015789
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.002478975
## Lag 2e+05 0.025290883
## Lag 3e+05 0.022907461
## Lag 4e+05 -0.020268594
## Lag 5e+05 -0.024916692
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.24548359
## Lag 2e+05 0.11052166
## Lag 3e+05 0.05432373
## Lag 4e+05 0.01805815
## Lag 5e+05 0.01153243
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.20488917 NaN
## Lag 2e+05 0.07941400 NaN
## Lag 3e+05 0.03173223 NaN
## Lag 4e+05 0.01207945 NaN
## Lag 5e+05 0.00937207 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.00000000 NaN
## Lag 1e+05 -0.027185814 0.06690508 NaN
## Lag 2e+05 0.003288122 0.01175922 NaN
## Lag 3e+05 -0.009807208 -0.02774368 NaN
## Lag 4e+05 0.028502131 -0.02004848 NaN
## Lag 5e+05 0.004224853 -0.03413976 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.01838116 -0.005016124 0.187090969
## Lag 2e+05 -0.01803243 0.006771741 0.067961712
## Lag 3e+05 -0.02990333 0.014055081 0.026932243
## Lag 4e+05 -0.01673471 -0.038591377 -0.006263361
## Lag 5e+05 -0.01010815 0.023505453 -0.020468104
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 0.180672090 0.142503791 0.08761510
## Lag 2e+05 0.075524754 0.058265141 0.01805396
## Lag 3e+05 0.061847868 0.024090601 0.00906693
## Lag 4e+05 0.026977571 0.009134784 -0.01343445
## Lag 5e+05 0.009238511 0.011288356 -0.04688926
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000e+00 NaN 1.00000000
## Lag 1e+05 7.567260e-02 NaN 0.19157351
## Lag 2e+05 9.798232e-03 NaN 0.09035011
## Lag 3e+05 -9.815243e-05 NaN 0.09173392
## Lag 4e+05 1.463847e-03 NaN 0.01895014
## Lag 5e+05 -8.933926e-03 NaN 0.02055896
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.0000000000 1.000000000
## Lag 1e+05 0.1092925302 0.064690009
## Lag 2e+05 0.0620218376 0.002791682
## Lag 3e+05 0.0195991952 -0.003413749
## Lag 4e+05 0.0001621148 -0.022908414
## Lag 5e+05 -0.0219618895 -0.059959659
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.106442451 0.22304679
## Lag 2e+05 0.054545171 0.11085315
## Lag 3e+05 0.010244780 0.05390366
## Lag 4e+05 0.008379283 0.04786309
## Lag 5e+05 0.001436509 0.01574710
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0187607101
## Lag 2e+05 0.0002399737
## Lag 3e+05 0.0073210093
## Lag 4e+05 -0.0148097631
## Lag 5e+05 0.0059137263
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0081020194
## Lag 2e+05 0.0008276591
## Lag 3e+05 0.0160971527
## Lag 4e+05 -0.0138968583
## Lag 5e+05 -0.0334847104
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.19619313
## Lag 2e+05 0.10398124
## Lag 3e+05 0.05240826
## Lag 4e+05 0.01139080
## Lag 5e+05 0.01371625
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.000000000 NaN
## Lag 1e+05 0.169365915 NaN
## Lag 2e+05 0.068535796 NaN
## Lag 3e+05 0.026832880 NaN
## Lag 4e+05 0.013426846 NaN
## Lag 5e+05 -0.005095864 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.0000000000 1.000000000 NaN
## Lag 1e+05 -0.0274154382 0.031315447 NaN
## Lag 2e+05 0.0081350637 0.002392960 NaN
## Lag 3e+05 0.0112026821 -0.016599074 NaN
## Lag 4e+05 -0.0009366492 -0.003546458 NaN
## Lag 5e+05 0.0110438988 -0.013107637 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.0000000000 1.000000000
## Lag 1e+05 0.016095865 -0.0009489891 0.139081885
## Lag 2e+05 -0.019184557 0.0057107212 0.076957529
## Lag 3e+05 0.006306646 -0.0064336694 0.018377866
## Lag 4e+05 0.009973494 -0.0371980918 0.021976716
## Lag 5e+05 -0.005375546 -0.0022381215 0.003570699
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.000000000 1.0000000000
## Lag 1e+05 0.167837104 0.127159838 0.0905548928
## Lag 2e+05 0.061230989 0.062415893 0.0478200022
## Lag 3e+05 0.044648689 0.027788126 0.0021618897
## Lag 4e+05 0.022859761 0.013991864 0.0170659031
## Lag 5e+05 0.005116968 0.002564168 -0.0008612144
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.07713654 NaN 0.18716084
## Lag 2e+05 0.04809892 NaN 0.08682495
## Lag 3e+05 -0.01414061 NaN 0.03460278
## Lag 4e+05 0.02001543 NaN 0.01142406
## Lag 5e+05 0.01264556 NaN 0.01317376
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.082205152 0.04032323
## Lag 2e+05 0.016563075 0.03330750
## Lag 3e+05 -0.006783307 0.01390140
## Lag 4e+05 -0.011532176 -0.01811727
## Lag 5e+05 -0.012201468 0.01440736
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.12800688 0.228846359
## Lag 2e+05 0.03640586 0.074019641
## Lag 3e+05 0.02193200 0.021605906
## Lag 4e+05 0.01781764 0.002147398
## Lag 5e+05 0.03012589 0.006600887
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.014249127
## Lag 2e+05 0.002788277
## Lag 3e+05 -0.006109748
## Lag 4e+05 0.013792129
## Lag 5e+05 0.003097483
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0064136409
## Lag 2e+05 -0.0156403287
## Lag 3e+05 0.0140782482
## Lag 4e+05 0.0081420585
## Lag 5e+05 -0.0001497984
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.21925900
## Lag 2e+05 0.10639515
## Lag 3e+05 0.05767041
## Lag 4e+05 0.03890008
## Lag 5e+05 0.03525085
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.0000000000 NaN
## Lag 1e+05 0.1760974068 NaN
## Lag 2e+05 0.0960475433 NaN
## Lag 3e+05 -0.0007477265 NaN
## Lag 4e+05 0.0142965277 NaN
## Lag 5e+05 0.0105918108 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 -0.007109414 0.072035015 NaN
## Lag 2e+05 -0.014039238 0.011703915 NaN
## Lag 3e+05 0.005964218 0.004433604 NaN
## Lag 4e+05 0.003000583 -0.013906887 NaN
## Lag 5e+05 -0.002389498 0.027072088 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 -0.001180558 -0.0177870546 0.15617883
## Lag 2e+05 0.004157036 -0.0237756911 0.04660473
## Lag 3e+05 -0.015685389 -0.0114954329 0.01819985
## Lag 4e+05 -0.002899550 -0.0159948860 0.01769235
## Lag 5e+05 0.019110711 -0.0005673584 0.02851493
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.000000000
## Lag 1e+05 0.21077731 0.13696625 0.093803187
## Lag 2e+05 0.10647140 0.04576534 0.040668977
## Lag 3e+05 0.04155740 0.02883993 0.009567086
## Lag 4e+05 0.03856546 0.01345383 -0.011941063
## Lag 5e+05 0.02688688 0.01106580 -0.006763714
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.073031460 NaN 0.17761114
## Lag 2e+05 0.023751034 NaN 0.03887062
## Lag 3e+05 0.008722027 NaN -0.00461939
## Lag 4e+05 0.015131722 NaN -0.02454995
## Lag 5e+05 0.030364306 NaN -0.03245683
## nodematch.race..wa.O absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000
## Lag 1e+05 0.12467017 0.073039137
## Lag 2e+05 0.04352901 0.001162365
## Lag 3e+05 0.03079318 0.008890373
## Lag 4e+05 0.01128837 -0.002510417
## Lag 5e+05 0.02350377 0.014697148
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8207 0.5785
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.4688 -0.8373
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.9742 -1.3980
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -2.2341
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 1.2678 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.1512 1.6010
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.3191 1.3428
## nodefactor.race..wa.H nodefactor.region.EW
## -0.5170 1.9403
## nodefactor.region.OW nodematch.race..wa.B
## 0.9870 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 1.1859 0.8032
## absdiff.sqrt.age
## 0.7218
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.41179574 0.56290220
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.63920686 0.40242237
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.04836395 0.16209996
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.02547739
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.20485390 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.87982287 0.10937466
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.74966366 0.17933074
## nodefactor.race..wa.H nodefactor.region.EW
## 0.60513651 0.05233799
## nodefactor.region.OW nodematch.race..wa.B
## 0.32364839 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.23565946 0.42188162
## absdiff.sqrt.age
## 0.47042355
## Joint P-value (lower = worse): 7.192737e-05 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.74678 0.36201
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.85052 1.49265
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.47319 0.05294
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.47253
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.68590 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.25552 -0.11910
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 1.01584 1.95508
## nodefactor.race..wa.H nodefactor.region.EW
## 0.17840 0.54138
## nodefactor.region.OW nodematch.race..wa.B
## 0.43254 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.43391 -0.22207
## absdiff.sqrt.age
## -0.12128
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.45519391 0.71734374
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.39503835 0.13552806
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.63607830 0.95777936
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.63654511
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.49277925 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.79832084 0.90519589
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.30970359 0.05057391
## nodefactor.race..wa.H nodefactor.region.EW
## 0.85841050 0.58824778
## nodefactor.region.OW nodematch.race..wa.B
## 0.66535065 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.66435265 0.82425563
## absdiff.sqrt.age
## 0.90346898
## Joint P-value (lower = worse): 0.9976415 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.24627 -0.63796
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.86356 1.38580
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.22927 0.22737
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.05179
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.97246 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.48807 0.02398
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.49475 1.97949
## nodefactor.race..wa.H nodefactor.region.EW
## -0.92833 -0.29298
## nodefactor.region.OW nodematch.race..wa.B
## -0.66338 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -1.44164 -0.41006
## absdiff.sqrt.age
## -0.09754
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.80547220 0.52350213
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.38783106 0.16580682
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.81866266 0.82013290
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.95869964
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.33082298 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.62550290 0.98086691
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.62077971 0.04776088
## nodefactor.race..wa.H nodefactor.region.EW
## 0.35323447 0.76953881
## nodefactor.region.OW nodematch.race..wa.B
## 0.50708679 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.14940282 0.68176070
## absdiff.sqrt.age
## 0.92229876
## Joint P-value (lower = worse): 0.627 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.7721 1.1876
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.9923 0.6276
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.0271 2.7477
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 1.1578
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.1084 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.1001 -0.3093
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.9169 -0.9699
## nodefactor.race..wa.H nodefactor.region.EW
## 1.0964 0.6150
## nodefactor.region.OW nodematch.race..wa.B
## 1.4477 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.8602 1.2264
## absdiff.sqrt.age
## 1.9215
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.440047066 0.234988533
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.002768609 0.530288765
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.304385475 0.006002069
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.246953574
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.913672067 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.920289006 0.757122745
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.359170227 0.332079431
## nodefactor.race..wa.H nodefactor.region.EW
## 0.272915254 0.538537377
## nodefactor.region.OW nodematch.race..wa.B
## 0.147693816 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.389673757 0.220035332
## absdiff.sqrt.age
## 0.054663907
## Joint P-value (lower = worse): 0.07127315 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.53730 -1.48822
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.56315 -0.42909
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -2.18686 -0.16159
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.94550
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.01468 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.14552 -0.96560
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.51938 -0.41085
## nodefactor.race..wa.H nodefactor.region.EW
## -0.02716 -0.82823
## nodefactor.region.OW nodematch.race..wa.B
## -1.14127 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.46499 -1.74729
## absdiff.sqrt.age
## -1.76675
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.12421964 0.13669196
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.01037269 0.66785625
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.02875283 0.87162722
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.34440116
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.98828433 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.88430075 0.33424283
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.12866654 0.68118208
## nodefactor.race..wa.H nodefactor.region.EW
## 0.97833492 0.40754036
## nodefactor.region.OW nodematch.race..wa.B
## 0.25375942 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.64193745 0.08058780
## absdiff.sqrt.age
## 0.07726946
## Joint P-value (lower = worse): 0.9898051 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.0988 0.6876
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.4478 0.5301
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.6821 0.7426
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.5208
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.5956 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.1728 -0.2371
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 1.0053 -0.0778
## nodefactor.race..wa.H nodefactor.region.EW
## 0.3872 -0.3609
## nodefactor.region.OW nodematch.race..wa.B
## -0.5479 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -0.3047 0.9401
## absdiff.sqrt.age
## 1.3498
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.2718768 0.4916787
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.1476708 0.5960120
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.4951679 0.4577357
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.6025236
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.5514438 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.8627787 0.8125556
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.3147541 0.9379835
## nodefactor.race..wa.H nodefactor.region.EW
## 0.6986437 0.7181730
## nodefactor.region.OW nodematch.race..wa.B
## 0.5837542 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.7606077 0.3471598
## absdiff.sqrt.age
## 0.1770947
## Joint P-value (lower = worse): 0.9915364 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.07970 0.50962
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.20269 -0.18958
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.14221 -0.60206
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.83506
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -1.02690 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -1.87010 -1.29421
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.74737 -0.66249
## nodefactor.race..wa.H nodefactor.region.EW
## 0.47761 -0.02569
## nodefactor.region.OW nodematch.race..wa.B
## 0.17669 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -0.08925 0.07375
## absdiff.sqrt.age
## 0.15811
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.93647731 0.61031565
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.83938008 0.84963758
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.25336583 0.54713119
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.40368578
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.30446622 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.06147027 0.19559156
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.45484323 0.50765862
## nodefactor.race..wa.H nodefactor.region.EW
## 0.63292581 0.97950527
## nodefactor.region.OW nodematch.race..wa.B
## 0.85974989 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.92888619 0.94121187
## absdiff.sqrt.age
## 0.87436988
## Joint P-value (lower = worse): 1.026273e-06 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.86872 0.05296
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.66440 0.47943
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.86342 -1.03401
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.60238
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.85162 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.71588 -0.14962
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.21514 -0.34448
## nodefactor.race..wa.H nodefactor.region.EW
## -1.15976 0.35454
## nodefactor.region.OW nodematch.race..wa.B
## -0.71856 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -1.18588 -0.33867
## absdiff.sqrt.age
## -0.83806
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.3850001 0.9577676
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.5064367 0.6316346
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3879090 0.3011319
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.5469236
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.3944235 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.4740640 0.8810611
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.2243131 0.7304878
## nodefactor.race..wa.H nodefactor.region.EW
## 0.2461447 0.7229339
## nodefactor.region.OW nodematch.race..wa.B
## 0.4724136 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2356682 0.7348579
## absdiff.sqrt.age
## 0.4019945
## Joint P-value (lower = worse): 0.980291 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Model 8
## Sample statistics summary:
##
## Iterations = 1e+06:375900000
## Thinning interval = 1e+05
## Number of chains = 8
## Sample size per chain = 3750
##
## 1. Empirical mean and standard deviation for each variable,
## plus standard error of the mean:
##
## Mean SD Naive SE Time-series SE
## edges -3.56556 21.732 0.12547 0.16451
## nodefactor.deg.main.deg.pers.0.1 0.58459 14.317 0.08266 0.13121
## nodefactor.deg.main.deg.pers.0.2 -0.02164 6.206 0.03583 0.03757
## nodefactor.deg.main.deg.pers.1.0 -0.13124 6.164 0.03559 0.03555
## nodefactor.deg.main.deg.pers.1.1 -0.19955 12.490 0.07211 0.10952
## nodefactor.deg.main.deg.pers.1.2 0.16013 12.950 0.07477 0.10936
## nodefactor.riskg.O2 -0.40092 0.000 0.00000 0.00000
## nodefactor.riskg.O3 -1.42575 2.358 0.01361 0.01340
## nodefactor.riskg.O4 -1.52093 11.560 0.06674 0.07646
## nodefactor.riskg.Y1 -1.34908 0.000 0.00000 0.00000
## nodefactor.riskg.Y2 -1.44488 2.612 0.01508 0.01514
## nodefactor.riskg.Y3 -1.48337 8.642 0.04989 0.04920
## nodefactor.riskg.Y4 -1.60022 36.939 0.21327 0.29480
## nodefactor.race..wa.B 6.38990 8.936 0.05159 0.07316
## nodefactor.race..wa.H 6.43339 10.927 0.06309 0.08694
## nodefactor.region.EW 0.05881 11.240 0.06490 0.11338
## nodefactor.region.OW -0.28319 20.318 0.11730 0.15196
## nodematch.race..wa.B -2.53754 0.000 0.00000 0.00000
## nodematch.race..wa.H -6.31580 2.637 0.01523 0.02307
## nodematch.race..wa.O 6.17662 17.091 0.09868 0.12570
## nodematch.region 0.10257 19.614 0.11324 0.15595
## absdiff.sqrt.age -0.13362 22.498 0.12989 0.14851
##
## 2. Quantiles for each variable:
##
## 2.5% 25% 50% 75% 97.5%
## edges -45.5122 -18.5122 -3.5122 10.4878 39.4878
## nodefactor.deg.main.deg.pers.0.1 -27.1754 -9.1754 0.8246 9.8246 29.8246
## nodefactor.deg.main.deg.pers.0.2 -11.2169 -4.2169 -0.2169 3.7831 12.7831
## nodefactor.deg.main.deg.pers.1.0 -11.5677 -4.5677 -0.5677 3.4323 12.4323
## nodefactor.deg.main.deg.pers.1.1 -24.3643 -8.3643 -0.3643 8.6357 24.6357
## nodefactor.deg.main.deg.pers.1.2 -24.8703 -8.8703 0.1297 9.1297 26.1297
## nodefactor.riskg.O2 -0.4009 -0.4009 -0.4009 -0.4009 -0.4009
## nodefactor.riskg.O3 -5.8558 -2.8558 -1.8558 0.1442 4.1442
## nodefactor.riskg.O4 -23.5127 -9.5127 -1.5127 6.4873 21.4873
## nodefactor.riskg.Y1 -1.3491 -1.3491 -1.3491 -1.3491 -1.3491
## nodefactor.riskg.Y2 -6.2024 -3.2024 -1.2024 -0.2024 3.7976
## nodefactor.riskg.Y3 -17.7860 -7.7860 -1.7860 4.2140 16.2140
## nodefactor.riskg.Y4 -73.0115 -27.0115 -2.0115 22.9885 71.9885
## nodefactor.race..wa.B -10.1865 -0.1865 5.8135 11.8135 23.8135
## nodefactor.race..wa.H -14.8353 -0.8353 6.1647 14.1647 28.1647
## nodefactor.region.EW -21.3887 -7.3887 -0.3887 7.6113 22.6113
## nodefactor.region.OW -39.1591 -14.1591 -0.1591 12.8409 40.8409
## nodematch.race..wa.B -2.5375 -2.5375 -2.5375 -2.5375 -2.5375
## nodematch.race..wa.H -11.2750 -8.2750 -6.2750 -4.2750 -0.2750
## nodematch.race..wa.O -26.8841 -5.8841 6.1159 17.1159 40.1159
## nodematch.region -37.8097 -12.8097 0.1903 13.1903 39.1903
## absdiff.sqrt.age -43.2664 -15.6723 -0.3485 14.9446 44.8308
##
##
## Sample statistics cross-correlations:## Warning in cor(as.matrix(x)): the standard deviation is zero## edges
## edges 1.0000000
## nodefactor.deg.main.deg.pers.0.1 0.5649033
## nodefactor.deg.main.deg.pers.0.2 0.2694434
## nodefactor.deg.main.deg.pers.1.0 0.2715611
## nodefactor.deg.main.deg.pers.1.1 0.5000955
## nodefactor.deg.main.deg.pers.1.2 0.5142969
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.1158591
## nodefactor.riskg.O4 0.4352814
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.1291005
## nodefactor.riskg.Y3 0.3664791
## nodefactor.riskg.Y4 0.9381541
## nodefactor.race..wa.B 0.4130760
## nodefactor.race..wa.H 0.4468128
## nodefactor.region.EW 0.3394088
## nodefactor.region.OW 0.5401817
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.1122183
## nodematch.race..wa.O 0.7871927
## nodematch.region 0.8972409
## absdiff.sqrt.age 0.7768734
## nodefactor.deg.main.deg.pers.0.1
## edges 0.56490326
## nodefactor.deg.main.deg.pers.0.1 1.00000000
## nodefactor.deg.main.deg.pers.0.2 0.08333554
## nodefactor.deg.main.deg.pers.1.0 0.08207671
## nodefactor.deg.main.deg.pers.1.1 0.14906535
## nodefactor.deg.main.deg.pers.1.2 0.14507264
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.05955529
## nodefactor.riskg.O4 0.25816084
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.07741975
## nodefactor.riskg.Y3 0.19732586
## nodefactor.riskg.Y4 0.52845477
## nodefactor.race..wa.B 0.23428473
## nodefactor.race..wa.H 0.20322226
## nodefactor.region.EW 0.18021199
## nodefactor.region.OW 0.28328868
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03848297
## nodematch.race..wa.O 0.47180050
## nodematch.region 0.50828937
## absdiff.sqrt.age 0.44651696
## nodefactor.deg.main.deg.pers.0.2
## edges 0.26944339
## nodefactor.deg.main.deg.pers.0.1 0.08333554
## nodefactor.deg.main.deg.pers.0.2 1.00000000
## nodefactor.deg.main.deg.pers.1.0 0.03603181
## nodefactor.deg.main.deg.pers.1.1 0.06563728
## nodefactor.deg.main.deg.pers.1.2 0.06562443
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.03407851
## nodefactor.riskg.O4 0.10132169
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.03882566
## nodefactor.riskg.Y3 0.10232870
## nodefactor.riskg.Y4 0.25646787
## nodefactor.race..wa.B 0.12133873
## nodefactor.race..wa.H 0.11507760
## nodefactor.region.EW 0.07865904
## nodefactor.region.OW 0.16534867
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.03166144
## nodematch.race..wa.O 0.21047234
## nodematch.region 0.24611996
## absdiff.sqrt.age 0.21474570
## nodefactor.deg.main.deg.pers.1.0
## edges 0.27156113
## nodefactor.deg.main.deg.pers.0.1 0.08207671
## nodefactor.deg.main.deg.pers.0.2 0.03603181
## nodefactor.deg.main.deg.pers.1.0 1.00000000
## nodefactor.deg.main.deg.pers.1.1 0.06518100
## nodefactor.deg.main.deg.pers.1.2 0.06873217
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.03918591
## nodefactor.riskg.O4 0.10932522
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.04048822
## nodefactor.riskg.Y3 0.10015884
## nodefactor.riskg.Y4 0.25651913
## nodefactor.race..wa.B 0.08633422
## nodefactor.race..wa.H 0.13702669
## nodefactor.region.EW 0.11046576
## nodefactor.region.OW 0.13101604
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.04850722
## nodematch.race..wa.O 0.22003338
## nodematch.region 0.24509129
## absdiff.sqrt.age 0.21756883
## nodefactor.deg.main.deg.pers.1.1
## edges 0.50009552
## nodefactor.deg.main.deg.pers.0.1 0.14906535
## nodefactor.deg.main.deg.pers.0.2 0.06563728
## nodefactor.deg.main.deg.pers.1.0 0.06518100
## nodefactor.deg.main.deg.pers.1.1 1.00000000
## nodefactor.deg.main.deg.pers.1.2 0.13266756
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.06786679
## nodefactor.riskg.O4 0.23778196
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.07421409
## nodefactor.riskg.Y3 0.19548549
## nodefactor.riskg.Y4 0.45870393
## nodefactor.race..wa.B 0.17006609
## nodefactor.race..wa.H 0.22836944
## nodefactor.region.EW 0.10401094
## nodefactor.region.OW 0.21882479
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.05367842
## nodematch.race..wa.O 0.40923899
## nodematch.region 0.46297568
## absdiff.sqrt.age 0.39276388
## nodefactor.deg.main.deg.pers.1.2
## edges 0.51429688
## nodefactor.deg.main.deg.pers.0.1 0.14507264
## nodefactor.deg.main.deg.pers.0.2 0.06562443
## nodefactor.deg.main.deg.pers.1.0 0.06873217
## nodefactor.deg.main.deg.pers.1.1 0.13266756
## nodefactor.deg.main.deg.pers.1.2 1.00000000
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.06476156
## nodefactor.riskg.O4 0.22262140
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.05998563
## nodefactor.riskg.Y3 0.17472635
## nodefactor.riskg.Y4 0.48621883
## nodefactor.race..wa.B 0.19236877
## nodefactor.race..wa.H 0.26595963
## nodefactor.region.EW 0.22527172
## nodefactor.region.OW 0.28780105
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.08270714
## nodematch.race..wa.O 0.39608106
## nodematch.region 0.45317539
## absdiff.sqrt.age 0.39000963
## nodefactor.riskg.O2 nodefactor.riskg.O3
## edges NA 0.115859142
## nodefactor.deg.main.deg.pers.0.1 NA 0.059555286
## nodefactor.deg.main.deg.pers.0.2 NA 0.034078505
## nodefactor.deg.main.deg.pers.1.0 NA 0.039185912
## nodefactor.deg.main.deg.pers.1.1 NA 0.067866789
## nodefactor.deg.main.deg.pers.1.2 NA 0.064761557
## nodefactor.riskg.O2 1 NA
## nodefactor.riskg.O3 NA 1.000000000
## nodefactor.riskg.O4 NA 0.044150017
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA 0.009015873
## nodefactor.riskg.Y3 NA 0.025499865
## nodefactor.riskg.Y4 NA 0.052070392
## nodefactor.race..wa.B NA 0.043012059
## nodefactor.race..wa.H NA 0.045628899
## nodefactor.region.EW NA 0.049651294
## nodefactor.region.OW NA 0.059828334
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H NA 0.005075216
## nodematch.race..wa.O NA 0.096439096
## nodematch.region NA 0.103399591
## absdiff.sqrt.age NA 0.121198388
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## edges 0.43528140 NA
## nodefactor.deg.main.deg.pers.0.1 0.25816084 NA
## nodefactor.deg.main.deg.pers.0.2 0.10132169 NA
## nodefactor.deg.main.deg.pers.1.0 0.10932522 NA
## nodefactor.deg.main.deg.pers.1.1 0.23778196 NA
## nodefactor.deg.main.deg.pers.1.2 0.22262140 NA
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.04415002 NA
## nodefactor.riskg.O4 1.00000000 NA
## nodefactor.riskg.Y1 NA 1
## nodefactor.riskg.Y2 0.02552813 NA
## nodefactor.riskg.Y3 0.07096897 NA
## nodefactor.riskg.Y4 0.17800276 NA
## nodefactor.race..wa.B 0.16448643 NA
## nodefactor.race..wa.H 0.15247803 NA
## nodefactor.region.EW 0.14849757 NA
## nodefactor.region.OW 0.22633338 NA
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.03223854 NA
## nodematch.race..wa.O 0.37495710 NA
## nodematch.region 0.39299182 NA
## absdiff.sqrt.age 0.43710916 NA
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## edges 0.129100526 0.36647910
## nodefactor.deg.main.deg.pers.0.1 0.077419751 0.19732586
## nodefactor.deg.main.deg.pers.0.2 0.038825665 0.10232870
## nodefactor.deg.main.deg.pers.1.0 0.040488220 0.10015884
## nodefactor.deg.main.deg.pers.1.1 0.074214092 0.19548549
## nodefactor.deg.main.deg.pers.1.2 0.059985630 0.17472635
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.009015873 0.02549987
## nodefactor.riskg.O4 0.025528127 0.07096897
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 1.000000000 0.02643596
## nodefactor.riskg.Y3 0.026435958 1.00000000
## nodefactor.riskg.Y4 0.066446585 0.17155634
## nodefactor.race..wa.B 0.045227229 0.14008228
## nodefactor.race..wa.H 0.055428556 0.17486182
## nodefactor.region.EW 0.042741542 0.13731074
## nodefactor.region.OW 0.074308806 0.20699243
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.014502400 0.04329698
## nodematch.race..wa.O 0.107306899 0.28762765
## nodematch.region 0.114717119 0.32645772
## absdiff.sqrt.age 0.098013314 0.27456098
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## edges 0.93815407 4.130760e-01
## nodefactor.deg.main.deg.pers.0.1 0.52845477 2.342847e-01
## nodefactor.deg.main.deg.pers.0.2 0.25646787 1.213387e-01
## nodefactor.deg.main.deg.pers.1.0 0.25651913 8.633422e-02
## nodefactor.deg.main.deg.pers.1.1 0.45870393 1.700661e-01
## nodefactor.deg.main.deg.pers.1.2 0.48621883 1.923688e-01
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.05207039 4.301206e-02
## nodefactor.riskg.O4 0.17800276 1.644864e-01
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 0.06644659 4.522723e-02
## nodefactor.riskg.Y3 0.17155634 1.400823e-01
## nodefactor.riskg.Y4 1.00000000 3.958486e-01
## nodefactor.race..wa.B 0.39584858 1.000000e+00
## nodefactor.race..wa.H 0.43027726 1.494934e-03
## nodefactor.region.EW 0.31457332 6.206241e-02
## nodefactor.region.OW 0.50726758 1.957536e-01
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.11047218 -9.592256e-03
## nodematch.race..wa.O 0.72786391 -5.077277e-05
## nodematch.region 0.84165381 3.825540e-01
## absdiff.sqrt.age 0.69840584 3.254149e-01
## nodefactor.race..wa.H
## edges 0.446812815
## nodefactor.deg.main.deg.pers.0.1 0.203222259
## nodefactor.deg.main.deg.pers.0.2 0.115077601
## nodefactor.deg.main.deg.pers.1.0 0.137026692
## nodefactor.deg.main.deg.pers.1.1 0.228369439
## nodefactor.deg.main.deg.pers.1.2 0.265959628
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.045628899
## nodefactor.riskg.O4 0.152478027
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.055428556
## nodefactor.riskg.Y3 0.174861822
## nodefactor.riskg.Y4 0.430277256
## nodefactor.race..wa.B 0.001494934
## nodefactor.race..wa.H 1.000000000
## nodefactor.region.EW 0.281683023
## nodefactor.region.OW 0.260614720
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.474413107
## nodematch.race..wa.O 0.001197067
## nodematch.region 0.380928125
## absdiff.sqrt.age 0.339887582
## nodefactor.region.EW nodefactor.region.OW
## edges 0.33940879 0.54018165
## nodefactor.deg.main.deg.pers.0.1 0.18021199 0.28328868
## nodefactor.deg.main.deg.pers.0.2 0.07865904 0.16534867
## nodefactor.deg.main.deg.pers.1.0 0.11046576 0.13101604
## nodefactor.deg.main.deg.pers.1.1 0.10401094 0.21882479
## nodefactor.deg.main.deg.pers.1.2 0.22527172 0.28780105
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 0.04965129 0.05982833
## nodefactor.riskg.O4 0.14849757 0.22633338
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 0.04274154 0.07430881
## nodefactor.riskg.Y3 0.13731074 0.20699243
## nodefactor.riskg.Y4 0.31457332 0.50726758
## nodefactor.race..wa.B 0.06206241 0.19575361
## nodefactor.race..wa.H 0.28168302 0.26061472
## nodefactor.region.EW 1.00000000 0.06838438
## nodefactor.region.OW 0.06838438 1.00000000
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H 0.11371495 0.07525120
## nodematch.race..wa.O 0.23656849 0.42949138
## nodematch.region 0.19200529 0.43613428
## absdiff.sqrt.age 0.26240787 0.41692937
## nodematch.race..wa.B nodematch.race..wa.H
## edges NA 0.112218258
## nodefactor.deg.main.deg.pers.0.1 NA 0.038482973
## nodefactor.deg.main.deg.pers.0.2 NA 0.031661438
## nodefactor.deg.main.deg.pers.1.0 NA 0.048507217
## nodefactor.deg.main.deg.pers.1.1 NA 0.053678420
## nodefactor.deg.main.deg.pers.1.2 NA 0.082707141
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 NA 0.005075216
## nodefactor.riskg.O4 NA 0.032238540
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 NA 0.014502400
## nodefactor.riskg.Y3 NA 0.043296983
## nodefactor.riskg.Y4 NA 0.110472182
## nodefactor.race..wa.B NA -0.009592256
## nodefactor.race..wa.H NA 0.474413107
## nodefactor.region.EW NA 0.113714953
## nodefactor.region.OW NA 0.075251196
## nodematch.race..wa.B 1 NA
## nodematch.race..wa.H NA 1.000000000
## nodematch.race..wa.O NA -0.001304986
## nodematch.region NA 0.090817019
## absdiff.sqrt.age NA 0.084850645
## nodematch.race..wa.O nodematch.region
## edges 7.871927e-01 0.89724092
## nodefactor.deg.main.deg.pers.0.1 4.718005e-01 0.50828937
## nodefactor.deg.main.deg.pers.0.2 2.104723e-01 0.24611996
## nodefactor.deg.main.deg.pers.1.0 2.200334e-01 0.24509129
## nodefactor.deg.main.deg.pers.1.1 4.092390e-01 0.46297568
## nodefactor.deg.main.deg.pers.1.2 3.960811e-01 0.45317539
## nodefactor.riskg.O2 NA NA
## nodefactor.riskg.O3 9.643910e-02 0.10339959
## nodefactor.riskg.O4 3.749571e-01 0.39299182
## nodefactor.riskg.Y1 NA NA
## nodefactor.riskg.Y2 1.073069e-01 0.11471712
## nodefactor.riskg.Y3 2.876277e-01 0.32645772
## nodefactor.riskg.Y4 7.278639e-01 0.84165381
## nodefactor.race..wa.B -5.077277e-05 0.38255396
## nodefactor.race..wa.H 1.197067e-03 0.38092813
## nodefactor.region.EW 2.365685e-01 0.19200529
## nodefactor.region.OW 4.294914e-01 0.43613428
## nodematch.race..wa.B NA NA
## nodematch.race..wa.H -1.304986e-03 0.09081702
## nodematch.race..wa.O 1.000000e+00 0.71131181
## nodematch.region 7.113118e-01 1.00000000
## absdiff.sqrt.age 6.134553e-01 0.69671813
## absdiff.sqrt.age
## edges 0.77687339
## nodefactor.deg.main.deg.pers.0.1 0.44651696
## nodefactor.deg.main.deg.pers.0.2 0.21474570
## nodefactor.deg.main.deg.pers.1.0 0.21756883
## nodefactor.deg.main.deg.pers.1.1 0.39276388
## nodefactor.deg.main.deg.pers.1.2 0.39000963
## nodefactor.riskg.O2 NA
## nodefactor.riskg.O3 0.12119839
## nodefactor.riskg.O4 0.43710916
## nodefactor.riskg.Y1 NA
## nodefactor.riskg.Y2 0.09801331
## nodefactor.riskg.Y3 0.27456098
## nodefactor.riskg.Y4 0.69840584
## nodefactor.race..wa.B 0.32541493
## nodefactor.race..wa.H 0.33988758
## nodefactor.region.EW 0.26240787
## nodefactor.region.OW 0.41692937
## nodematch.race..wa.B NA
## nodematch.race..wa.H 0.08485065
## nodematch.race..wa.O 0.61345534
## nodematch.region 0.69671813
## absdiff.sqrt.age 1.00000000
##
## Sample statistics auto-correlation:
## Chain 1
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.199796079 0.33823053
## Lag 2e+05 0.095443735 0.17869589
## Lag 3e+05 0.041721041 0.11585134
## Lag 4e+05 0.010948984 0.08602792
## Lag 5e+05 -0.002032955 0.04937575
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.040981629
## Lag 2e+05 0.007964895
## Lag 3e+05 -0.001863830
## Lag 4e+05 -0.009155471
## Lag 5e+05 0.005612414
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003443310
## Lag 2e+05 -0.007021287
## Lag 3e+05 0.006427541
## Lag 4e+05 -0.006392591
## Lag 5e+05 -0.015822543
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30123266
## Lag 2e+05 0.19036446
## Lag 3e+05 0.10649332
## Lag 4e+05 0.07351287
## Lag 5e+05 0.06191502
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.27846980 NaN
## Lag 2e+05 0.13594240 NaN
## Lag 3e+05 0.06738239 NaN
## Lag 4e+05 0.03219676 NaN
## Lag 5e+05 0.04452777 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.00000000 NaN
## Lag 1e+05 0.009663466 0.09997852 NaN
## Lag 2e+05 0.016374387 0.03261030 NaN
## Lag 3e+05 0.003778940 0.03528523 NaN
## Lag 4e+05 0.007049766 0.01116145 NaN
## Lag 5e+05 0.006327473 -0.00914574 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.021849023 0.009842728 0.239236994
## Lag 2e+05 0.037321516 -0.017829106 0.115849782
## Lag 3e+05 -0.025961507 -0.033475446 0.052777266
## Lag 4e+05 0.008635441 -0.002559349 0.023737626
## Lag 5e+05 -0.036371709 -0.001551359 0.003142448
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.239332373 0.19100467 0.30309940
## Lag 2e+05 0.104517654 0.12042639 0.20262472
## Lag 3e+05 0.076501952 0.04787429 0.14048014
## Lag 4e+05 0.014299375 0.01311789 0.10352004
## Lag 5e+05 0.006650576 0.03330294 0.08455149
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.170847216 NaN 0.25743237
## Lag 2e+05 0.070545448 NaN 0.13019086
## Lag 3e+05 0.043334352 NaN 0.08650448
## Lag 4e+05 0.007472401 NaN 0.04920890
## Lag 5e+05 0.022481750 NaN 0.03777267
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.000000000 1.000000000
## Lag 1e+05 0.18341124 0.249838586 0.105832513
## Lag 2e+05 0.08935911 0.124793929 0.054141220
## Lag 3e+05 0.03650999 0.063592661 0.024150383
## Lag 4e+05 0.01934678 0.020323618 0.009628635
## Lag 5e+05 0.01361759 0.007312366 -0.015354250
## Chain 2
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.00000000
## Lag 1e+05 0.194532678 0.32612400
## Lag 2e+05 0.079747937 0.15464800
## Lag 3e+05 0.052295881 0.09592679
## Lag 4e+05 0.014848555 0.04416558
## Lag 5e+05 -0.008018408 0.02052307
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.038011968
## Lag 2e+05 -0.000219702
## Lag 3e+05 0.020734355
## Lag 4e+05 -0.004666774
## Lag 5e+05 0.012941376
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.015158172
## Lag 2e+05 0.004655728
## Lag 3e+05 -0.013043665
## Lag 4e+05 0.006811322
## Lag 5e+05 0.016601321
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.29125623
## Lag 2e+05 0.15961505
## Lag 3e+05 0.09612892
## Lag 4e+05 0.03795094
## Lag 5e+05 0.03317365
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.26785140 NaN
## Lag 2e+05 0.15112241 NaN
## Lag 3e+05 0.08536794 NaN
## Lag 4e+05 0.06648910 NaN
## Lag 5e+05 0.02141531 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.00000000 1.000000000 NaN
## Lag 1e+05 -0.02382610 0.090288470 NaN
## Lag 2e+05 -0.01616006 0.028970283 NaN
## Lag 3e+05 -0.01194960 0.026091581 NaN
## Lag 4e+05 0.01863682 0.000627544 NaN
## Lag 5e+05 0.01048361 -0.018164753 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 -0.004373577 -0.003913656 0.244545580
## Lag 2e+05 0.006687090 -0.047642205 0.116674745
## Lag 3e+05 0.027532041 0.015112247 0.047891811
## Lag 4e+05 -0.004486563 0.027720611 0.017406518
## Lag 5e+05 -0.003934316 0.012496895 0.008775215
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.21709727 0.211763315 0.30094840
## Lag 2e+05 0.10202676 0.102150474 0.19244994
## Lag 3e+05 0.05679021 0.071424645 0.13784798
## Lag 4e+05 0.02361915 0.007648035 0.07758516
## Lag 5e+05 0.03679189 -0.008274849 0.05840279
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.16305862 NaN 0.26332223
## Lag 2e+05 0.07467225 NaN 0.12044739
## Lag 3e+05 0.03938576 NaN 0.09496718
## Lag 4e+05 0.01357288 NaN 0.02835765
## Lag 5e+05 -0.02363513 NaN 0.04477631
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000e+00
## Lag 1e+05 0.167273514 0.226235443 1.208087e-01
## Lag 2e+05 0.072944155 0.096175455 1.424128e-02
## Lag 3e+05 0.043473502 0.072548781 3.504512e-05
## Lag 4e+05 0.029809331 0.020371650 -1.572370e-02
## Lag 5e+05 -0.007923479 0.003525331 -1.810176e-02
## Chain 3
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.19159519 0.31640603
## Lag 2e+05 0.06817764 0.15494078
## Lag 3e+05 0.05477417 0.09494944
## Lag 4e+05 0.03121814 0.06964662
## Lag 5e+05 0.04837717 0.04740504
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0210495030
## Lag 2e+05 0.0007972085
## Lag 3e+05 -0.0074049461
## Lag 4e+05 0.0121456934
## Lag 5e+05 -0.0294604135
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 -0.003893960
## Lag 2e+05 -0.008973501
## Lag 3e+05 0.002366056
## Lag 4e+05 -0.022061648
## Lag 5e+05 -0.003774530
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.30834774
## Lag 2e+05 0.14711474
## Lag 3e+05 0.09110987
## Lag 4e+05 0.04338031
## Lag 5e+05 0.02832502
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.23701919 NaN
## Lag 2e+05 0.08104542 NaN
## Lag 3e+05 0.07885246 NaN
## Lag 4e+05 0.04169604 NaN
## Lag 5e+05 0.03471014 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.0000000000 1.00000000 NaN
## Lag 1e+05 0.0055830889 0.10428240 NaN
## Lag 2e+05 0.0001676994 0.02941532 NaN
## Lag 3e+05 -0.0069607787 0.01024346 NaN
## Lag 4e+05 -0.0089027749 0.01987420 NaN
## Lag 5e+05 0.0161722540 0.03221686 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.0000000000 1.000000000 1.00000000
## Lag 1e+05 -0.0105868429 0.002386419 0.23927333
## Lag 2e+05 0.0005818507 -0.013135539 0.09594067
## Lag 3e+05 -0.0106934998 -0.003354261 0.06506249
## Lag 4e+05 -0.0124726764 -0.021606757 0.04282431
## Lag 5e+05 -0.0042980015 0.009664295 0.05071711
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.22713028 0.20615104 0.28320225
## Lag 2e+05 0.12832070 0.09152105 0.18434255
## Lag 3e+05 0.07215433 0.05836968 0.13367295
## Lag 4e+05 0.07170997 0.02660480 0.09717251
## Lag 5e+05 0.03256345 0.02425171 0.06226597
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.21137095 NaN 0.24647043
## Lag 2e+05 0.10718583 NaN 0.11709445
## Lag 3e+05 0.07683349 NaN 0.06430806
## Lag 4e+05 0.05580938 NaN 0.03609187
## Lag 5e+05 0.04610206 NaN -0.01035316
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.18050165 0.21766424 0.09955475
## Lag 2e+05 0.06323102 0.09474840 0.02924141
## Lag 3e+05 0.04354017 0.06605972 0.02642329
## Lag 4e+05 0.02114070 0.03506386 0.00946626
## Lag 5e+05 0.01634583 0.04844222 0.02643047
## Chain 4
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.19508974 0.30399777
## Lag 2e+05 0.09561936 0.17172503
## Lag 3e+05 0.04533624 0.11976034
## Lag 4e+05 0.04045804 0.07312180
## Lag 5e+05 0.01682589 0.08161597
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0570494911
## Lag 2e+05 0.0136460880
## Lag 3e+05 -0.0049530068
## Lag 4e+05 -0.0005274976
## Lag 5e+05 -0.0042729297
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.0000000000
## Lag 1e+05 0.0005910585
## Lag 2e+05 0.0020307249
## Lag 3e+05 -0.0461998311
## Lag 4e+05 0.0092410355
## Lag 5e+05 0.0055531571
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.33059437
## Lag 2e+05 0.18153493
## Lag 3e+05 0.08900810
## Lag 4e+05 0.04708687
## Lag 5e+05 0.02967748
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.26611716 NaN
## Lag 2e+05 0.12113240 NaN
## Lag 3e+05 0.07769468 NaN
## Lag 4e+05 0.04917169 NaN
## Lag 5e+05 0.03657866 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000e+00 1.00000000 NaN
## Lag 1e+05 -4.908333e-03 0.09725251 NaN
## Lag 2e+05 7.712042e-05 0.02221627 NaN
## Lag 3e+05 -1.866551e-02 0.03465193 NaN
## Lag 4e+05 2.586054e-02 0.02005784 NaN
## Lag 5e+05 -1.105331e-02 0.00468463 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.001100492 0.0107397172 0.24199901
## Lag 2e+05 0.013450788 0.0161079785 0.10763744
## Lag 3e+05 -0.024139047 -0.0044654976 0.04192610
## Lag 4e+05 -0.014011785 0.0009975267 0.03899474
## Lag 5e+05 -0.007906765 0.0095031603 0.02214853
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.205172331 0.17953038 0.29438739
## Lag 2e+05 0.087367242 0.11243362 0.19859988
## Lag 3e+05 0.066918428 0.09641134 0.14488042
## Lag 4e+05 0.067204376 0.07139344 0.10729392
## Lag 5e+05 0.006020644 0.04083295 0.06815222
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.18754513 NaN 0.26829534
## Lag 2e+05 0.08460125 NaN 0.17056742
## Lag 3e+05 0.05032405 NaN 0.11415735
## Lag 4e+05 0.04636697 NaN 0.10303574
## Lag 5e+05 0.02065906 NaN 0.06580521
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 0.193779768 0.23541116 0.09977578
## Lag 2e+05 0.100719360 0.10672189 0.04935837
## Lag 3e+05 0.053280728 0.05969507 0.04330065
## Lag 4e+05 0.017945009 0.05741005 0.01727235
## Lag 5e+05 -0.001894853 0.02939976 0.01313848
## Chain 5
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.18407732 0.29207536
## Lag 2e+05 0.08092445 0.15720160
## Lag 3e+05 0.03624481 0.09584078
## Lag 4e+05 0.01407873 0.06339874
## Lag 5e+05 0.02544165 0.03459010
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.0000000000
## Lag 1e+05 0.0526919998
## Lag 2e+05 0.0262004267
## Lag 3e+05 -0.0006769669
## Lag 4e+05 -0.0073265027
## Lag 5e+05 -0.0169482747
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.012172873
## Lag 2e+05 0.016775712
## Lag 3e+05 -0.003324037
## Lag 4e+05 0.022773895
## Lag 5e+05 0.040919784
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.31481272
## Lag 2e+05 0.16520252
## Lag 3e+05 0.08796297
## Lag 4e+05 0.06273355
## Lag 5e+05 0.03719886
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.24812207 NaN
## Lag 2e+05 0.11233507 NaN
## Lag 3e+05 0.05850476 NaN
## Lag 4e+05 0.04305759 NaN
## Lag 5e+05 0.05396184 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 -0.027313676 0.091806599 NaN
## Lag 2e+05 0.026236161 0.057399394 NaN
## Lag 3e+05 0.002896822 -0.010161301 NaN
## Lag 4e+05 -0.002516686 0.005726486 NaN
## Lag 5e+05 0.022460136 -0.020602137 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.00000000 1.00000000
## Lag 1e+05 -0.025604856 -0.01456240 0.23207456
## Lag 2e+05 0.004541738 -0.01556903 0.10606107
## Lag 3e+05 0.008183799 -0.03740247 0.05332398
## Lag 4e+05 0.015190266 0.01984379 0.02921620
## Lag 5e+05 -0.004910979 0.01043950 0.03901637
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.0000000
## Lag 1e+05 0.23315077 0.23364990 0.2816848
## Lag 2e+05 0.12799418 0.13203565 0.1984260
## Lag 3e+05 0.07153435 0.07126792 0.1649331
## Lag 4e+05 0.01794682 0.01508743 0.1192896
## Lag 5e+05 0.01812468 0.02619630 0.1026080
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.19305664 NaN 0.20929876
## Lag 2e+05 0.07105775 NaN 0.12101960
## Lag 3e+05 0.02744183 NaN 0.06911960
## Lag 4e+05 -0.01262275 NaN 0.04759661
## Lag 5e+05 -0.01581832 NaN 0.03074158
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.0000000000
## Lag 1e+05 0.14833761 0.20865936 0.1063360159
## Lag 2e+05 0.05232222 0.08748158 0.0621543237
## Lag 3e+05 0.01236513 0.03525519 0.0155328968
## Lag 4e+05 0.02472844 0.02175787 -0.0132763917
## Lag 5e+05 0.03614763 0.02117444 0.0004385869
## Chain 6
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.18097969 0.28425586
## Lag 2e+05 0.08677275 0.14803187
## Lag 3e+05 0.07974076 0.10578425
## Lag 4e+05 0.05450908 0.06876284
## Lag 5e+05 0.03978584 0.04179915
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.047544204
## Lag 2e+05 0.014920849
## Lag 3e+05 0.009121961
## Lag 4e+05 0.013463720
## Lag 5e+05 -0.004296466
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.029670803
## Lag 2e+05 0.017779997
## Lag 3e+05 0.028130999
## Lag 4e+05 0.004441709
## Lag 5e+05 0.003070608
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.29636608
## Lag 2e+05 0.17537688
## Lag 3e+05 0.09071157
## Lag 4e+05 0.07080560
## Lag 5e+05 0.05089448
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.27819095 NaN
## Lag 2e+05 0.12812944 NaN
## Lag 3e+05 0.09771414 NaN
## Lag 4e+05 0.05770162 NaN
## Lag 5e+05 0.05233042 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 -0.010262448 0.106634919 NaN
## Lag 2e+05 0.001103472 0.049853057 NaN
## Lag 3e+05 -0.006496813 -0.029428664 NaN
## Lag 4e+05 -0.007063926 0.005664593 NaN
## Lag 5e+05 0.010328523 0.047930052 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.000000000 1.00000000
## Lag 1e+05 -0.006526363 0.012163730 0.23588593
## Lag 2e+05 0.004865949 0.003212733 0.10769224
## Lag 3e+05 -0.011715660 0.023986281 0.10674975
## Lag 4e+05 0.001235451 0.010046447 0.08018892
## Lag 5e+05 -0.009625251 -0.005246617 0.04146183
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.0000000000 1.00000000 1.00000000
## Lag 1e+05 0.2211386103 0.22288160 0.31956481
## Lag 2e+05 0.0959221501 0.10365828 0.21757062
## Lag 3e+05 0.0630784988 0.06390551 0.13934087
## Lag 4e+05 0.0222374078 0.04979931 0.08966756
## Lag 5e+05 0.0006070651 0.01430520 0.08481844
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.00000000 NaN 1.00000000
## Lag 1e+05 0.20830556 NaN 0.28689227
## Lag 2e+05 0.09839399 NaN 0.15821494
## Lag 3e+05 0.07775237 NaN 0.07549363
## Lag 4e+05 0.03555281 NaN 0.07298278
## Lag 5e+05 0.02853138 NaN 0.02763284
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.15711614 0.20386911 0.10720211
## Lag 2e+05 0.07068030 0.09164690 0.03226088
## Lag 3e+05 0.04514573 0.07073131 0.03667348
## Lag 4e+05 0.03813517 0.06093778 0.01541817
## Lag 5e+05 0.04477554 0.02782266 0.01536491
## Chain 7
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.00000000 1.00000000
## Lag 1e+05 0.17987958 0.30418551
## Lag 2e+05 0.06616458 0.17234295
## Lag 3e+05 0.04062654 0.10317151
## Lag 4e+05 0.02603462 0.05919729
## Lag 5e+05 0.01811805 0.04419383
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.068624980
## Lag 2e+05 -0.004403662
## Lag 3e+05 0.008852522
## Lag 4e+05 -0.022454676
## Lag 5e+05 0.019557673
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.005980916
## Lag 2e+05 0.003514619
## Lag 3e+05 0.009728776
## Lag 4e+05 0.004190767
## Lag 5e+05 -0.037883866
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.29563161
## Lag 2e+05 0.12540717
## Lag 3e+05 0.09124414
## Lag 4e+05 0.06317559
## Lag 5e+05 0.01972196
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.27973077 NaN
## Lag 2e+05 0.13667845 NaN
## Lag 3e+05 0.05669814 NaN
## Lag 4e+05 0.06634228 NaN
## Lag 5e+05 0.02854105 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 0.002477492 0.099375890 NaN
## Lag 2e+05 -0.001152193 0.028544575 NaN
## Lag 3e+05 -0.010457892 0.020409249 NaN
## Lag 4e+05 -0.015172438 0.034234793 NaN
## Lag 5e+05 -0.017815328 0.005020669 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.000000000 1.0000000000 1.00000000
## Lag 1e+05 0.053212406 0.0055569907 0.22946707
## Lag 2e+05 0.022321109 0.0150806564 0.08820948
## Lag 3e+05 -0.025293211 -0.0005585168 0.05291578
## Lag 4e+05 0.003421593 -0.0213551194 0.03893897
## Lag 5e+05 0.011371136 0.0104686809 0.02677868
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.23941875 0.24142672 0.31944545
## Lag 2e+05 0.12516811 0.10554649 0.20885308
## Lag 3e+05 0.07643474 0.04744658 0.14390528
## Lag 4e+05 0.02527472 0.04687684 0.08801910
## Lag 5e+05 0.03709380 0.01866032 0.05128301
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.198573201 NaN 0.26761012
## Lag 2e+05 0.081632207 NaN 0.12539122
## Lag 3e+05 0.040214750 NaN 0.07585451
## Lag 4e+05 0.013625324 NaN 0.05130556
## Lag 5e+05 0.004600815 NaN 0.02661434
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.00000000 1.00000000 1.00000000
## Lag 1e+05 0.17882194 0.20621276 0.11169881
## Lag 2e+05 0.08135737 0.08466386 0.02669493
## Lag 3e+05 0.03397391 0.06271596 0.02094325
## Lag 4e+05 0.02257184 0.04155520 0.02620520
## Lag 5e+05 0.03901024 0.02078500 0.02197635
## Chain 8
## edges nodefactor.deg.main.deg.pers.0.1
## Lag 0 1.000000000 1.000000000
## Lag 1e+05 0.192226484 0.305088334
## Lag 2e+05 0.096615876 0.144467846
## Lag 3e+05 0.046707721 0.085895073
## Lag 4e+05 -0.002569032 0.040136388
## Lag 5e+05 -0.014328786 0.009102839
## nodefactor.deg.main.deg.pers.0.2
## Lag 0 1.000000000
## Lag 1e+05 0.043935021
## Lag 2e+05 -0.002589349
## Lag 3e+05 0.009026611
## Lag 4e+05 0.001305383
## Lag 5e+05 0.001052281
## nodefactor.deg.main.deg.pers.1.0
## Lag 0 1.000000000
## Lag 1e+05 0.007984594
## Lag 2e+05 0.008422647
## Lag 3e+05 -0.012611203
## Lag 4e+05 -0.012286644
## Lag 5e+05 -0.028827041
## nodefactor.deg.main.deg.pers.1.1
## Lag 0 1.00000000
## Lag 1e+05 0.27896124
## Lag 2e+05 0.14878732
## Lag 3e+05 0.07917982
## Lag 4e+05 0.05885601
## Lag 5e+05 0.01712633
## nodefactor.deg.main.deg.pers.1.2 nodefactor.riskg.O2
## Lag 0 1.00000000 NaN
## Lag 1e+05 0.25791066 NaN
## Lag 2e+05 0.11937180 NaN
## Lag 3e+05 0.06575066 NaN
## Lag 4e+05 0.04425693 NaN
## Lag 5e+05 0.05455539 NaN
## nodefactor.riskg.O3 nodefactor.riskg.O4 nodefactor.riskg.Y1
## Lag 0 1.000000000 1.000000000 NaN
## Lag 1e+05 -0.005083331 0.135371224 NaN
## Lag 2e+05 -0.020299195 0.063081299 NaN
## Lag 3e+05 0.012052156 0.012488064 NaN
## Lag 4e+05 -0.036204151 -0.005652948 NaN
## Lag 5e+05 0.004609006 -0.030604273 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3 nodefactor.riskg.Y4
## Lag 0 1.0000000000 1.000000000 1.000000000
## Lag 1e+05 -0.0001391938 0.016572586 0.233240346
## Lag 2e+05 0.0188088576 0.001763158 0.110101676
## Lag 3e+05 -0.0050238976 0.009389993 0.047180565
## Lag 4e+05 0.0034055021 0.012039326 0.006186067
## Lag 5e+05 0.0106706695 -0.002446455 0.000659803
## nodefactor.race..wa.B nodefactor.race..wa.H nodefactor.region.EW
## Lag 0 1.00000000 1.000000000 1.00000000
## Lag 1e+05 0.26293392 0.249300220 0.32926922
## Lag 2e+05 0.13702576 0.123348831 0.20895278
## Lag 3e+05 0.08836017 0.053256715 0.17321705
## Lag 4e+05 0.04151497 0.015581184 0.11576380
## Lag 5e+05 0.01515828 -0.006345339 0.07549173
## nodefactor.region.OW nodematch.race..wa.B nodematch.race..wa.H
## Lag 0 1.000000000 NaN 1.00000000
## Lag 1e+05 0.180587497 NaN 0.29708559
## Lag 2e+05 0.069157396 NaN 0.16312855
## Lag 3e+05 0.039457266 NaN 0.10947573
## Lag 4e+05 0.011311086 NaN 0.08204621
## Lag 5e+05 -0.002471714 NaN 0.03040432
## nodematch.race..wa.O nodematch.region absdiff.sqrt.age
## Lag 0 1.000000000 1.000000000 1.000000000
## Lag 1e+05 0.168836613 0.239920368 0.103186778
## Lag 2e+05 0.093414241 0.118572091 0.033728852
## Lag 3e+05 0.037204513 0.062383002 0.009477534
## Lag 4e+05 0.002495196 0.008995628 -0.012701860
## Lag 5e+05 -0.024306741 -0.010361522 -0.028780925
##
## Sample statistics burn-in diagnostic (Geweke):## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.
## Warning in approx.hotelling.diff.test(x1, x2, var.equal = TRUE): Vector(s)
## do not vary but equal mu0; they have been ignored for the purposes of
## testing.## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 0.58586 0.04636
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -2.31370 0.79421
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.74784 1.39761
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.77301
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.02243 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.09086 0.29405
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.58380 -0.34330
## nodefactor.race..wa.H nodefactor.region.EW
## 1.70129 2.25988
## nodefactor.region.OW nodematch.race..wa.B
## 0.14886 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.63245 -0.07657
## nodematch.region absdiff.sqrt.age
## 0.81347 -0.27055
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.55796962 0.96302137
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.02068445 0.42707465
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.45455692 0.16223115
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.43951665
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.98210470 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.92760031 0.76871683
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.55935709 0.73137105
## nodefactor.race..wa.H nodefactor.region.EW
## 0.08888791 0.02382843
## nodefactor.region.OW nodematch.race..wa.B
## 0.88166218 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.52709581 0.93896726
## nodematch.region absdiff.sqrt.age
## 0.41594899 0.78674020
## Joint P-value (lower = worse): 0.6264776 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.29628 0.05266
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -1.41676 3.06377
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.51303 1.95912
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -0.15477
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 4.16787 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 1.14993 -0.87783
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.53778 1.47790
## nodefactor.race..wa.H nodefactor.region.EW
## -0.45452 0.51090
## nodefactor.region.OW nodematch.race..wa.B
## 0.40816 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -1.08848 1.24367
## nodematch.region absdiff.sqrt.age
## 1.66429 0.91940
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 1.948777e-01 9.580023e-01
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.565524e-01 2.185653e-03
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 6.079311e-01 5.009839e-02
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 8.770048e-01
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 3.074603e-05 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 2.501733e-01 3.800372e-01
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 5.907286e-01 1.394340e-01
## nodefactor.race..wa.H nodefactor.region.EW
## 6.494543e-01 6.094201e-01
## nodefactor.region.OW nodematch.race..wa.B
## 6.831539e-01 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 2.763834e-01 2.136207e-01
## nodematch.region absdiff.sqrt.age
## 9.605380e-02 3.578838e-01
## Joint P-value (lower = worse): 0.0002176157 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.2454 -0.8283
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 1.2592 -0.7902
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.1869 -3.6736
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -1.8569
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.3341 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 1.9363 -0.8651
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.3613 -1.4924
## nodefactor.race..wa.H nodefactor.region.EW
## -0.3741 -2.1222
## nodefactor.region.OW nodematch.race..wa.B
## 0.2405 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -0.9402 -0.9044
## nodematch.region absdiff.sqrt.age
## -1.9105 -0.5343
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.2129974437 0.4075039322
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.2079681963 0.4293835414
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.8517506252 0.0002391221
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.0633247033
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.7382671986 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.0528321748 0.3869966955
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.1734062164 0.1355824612
## nodefactor.race..wa.H nodefactor.region.EW
## 0.7083409440 0.0338209916
## nodefactor.region.OW nodematch.race..wa.B
## 0.8099417678 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.3471254349 0.3657943591
## nodematch.region absdiff.sqrt.age
## 0.0560686207 0.5931341461
## Joint P-value (lower = worse): 3.548353e-07 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.48936 -0.21638
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.31582 -0.79379
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.28546 -1.11934
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -1.01205
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -1.09989 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.44359 1.48919
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -0.47508 -0.06266
## nodefactor.race..wa.H nodefactor.region.EW
## -1.20052 -1.09855
## nodefactor.region.OW nodematch.race..wa.B
## 1.42565 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -0.40499 0.15812
## nodematch.region absdiff.sqrt.age
## -0.65263 -0.31392
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.6245891 0.8286937
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.7521368 0.4273179
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.7752926 0.2629933
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.3115165
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.2713802 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.6573412 0.1364381
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.6347316 0.9500339
## nodefactor.race..wa.H nodefactor.region.EW
## 0.2299389 0.2719622
## nodefactor.region.OW nodematch.race..wa.B
## 0.1539700 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.6854820 0.8743584
## nodematch.region absdiff.sqrt.age
## 0.5139954 0.7535795
## Joint P-value (lower = worse): 0.8460811 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.0857 -1.4754
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.1683 -1.8131
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -0.1793 -0.9198
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.2109
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -0.1719 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.1663 -0.9285
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -0.9722 0.7286
## nodefactor.race..wa.H nodefactor.region.EW
## -0.6054 -0.5174
## nodefactor.region.OW nodematch.race..wa.B
## -0.9202 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.4782 -1.3780
## nodematch.region absdiff.sqrt.age
## -0.6634 -1.1113
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.27761840 0.14009648
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.86633529 0.06981048
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.85767044 0.35766747
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.83292718
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.86355493 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.86790476 0.35316076
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.33093618 0.46621988
## nodefactor.race..wa.H nodefactor.region.EW
## 0.54490012 0.60489939
## nodefactor.region.OW nodematch.race..wa.B
## 0.35746433 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.63251446 0.16819204
## nodematch.region absdiff.sqrt.age
## 0.50706633 0.26643093
## Joint P-value (lower = worse): 0.9736067 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -0.17484 0.06719
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## -0.49540 0.58297
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.84212 -0.56957
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.29477
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## -1.41999 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -1.43625 -0.03870
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.20802 0.35902
## nodefactor.race..wa.H nodefactor.region.EW
## 0.07009 1.57137
## nodefactor.region.OW nodematch.race..wa.B
## -0.37015 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## -0.30350 -0.40011
## nodematch.region absdiff.sqrt.age
## -0.36663 -0.57050
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.8612090 0.9464303
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.6203151 0.5599110
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.3997208 0.5689718
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.7681717
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.1556095 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.1509306 0.9691257
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.8352112 0.7195835
## nodefactor.race..wa.H nodefactor.region.EW
## 0.9441259 0.1160971
## nodefactor.region.OW nodematch.race..wa.B
## 0.7112678 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.7615088 0.6890774
## nodematch.region absdiff.sqrt.age
## 0.7138941 0.5683407
## Joint P-value (lower = worse): 0.9659163 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## 1.8164 1.1045
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.9034 0.2677
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 1.5342 -0.3186
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.6488
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 2.9017 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.5894 0.1196
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 1.0811 0.5606
## nodefactor.race..wa.H nodefactor.region.EW
## 0.3993 1.1820
## nodefactor.region.OW nodematch.race..wa.B
## 1.9794 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.2143 1.5214
## nodematch.region absdiff.sqrt.age
## 1.4462 2.3482
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.069314366 0.269396181
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.366311842 0.788945019
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.124987596 0.750053545
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.516452893
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.003711976 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.555595983 0.904801619
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.279634887 0.575064616
## nodefactor.race..wa.H nodefactor.region.EW
## 0.689693612 0.237205411
## nodefactor.region.OW nodematch.race..wa.B
## 0.047770375 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.830313226 0.128167887
## nodematch.region absdiff.sqrt.age
## 0.148131849 0.018866778
## Joint P-value (lower = worse): 0.227469 .
## Chain 8
##
## Fraction in 1st window = 0.1
## Fraction in 2nd window = 0.5
##
## edges nodefactor.deg.main.deg.pers.0.1
## -1.53292 -2.43825
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.37620 -1.15875
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## -1.51114 -1.22836
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN -1.73211
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.21532 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## -0.17301 -1.25302
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## -1.49534 -1.25172
## nodefactor.race..wa.H nodefactor.region.EW
## -0.45148 -2.05302
## nodefactor.region.OW nodematch.race..wa.B
## -0.26572 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.07179 -1.02375
## nodematch.region absdiff.sqrt.age
## -1.13080 0.49391
##
## Individual P-values (lower = worse):
## edges nodefactor.deg.main.deg.pers.0.1
## 0.12529533 0.01475852
## nodefactor.deg.main.deg.pers.0.2 nodefactor.deg.main.deg.pers.1.0
## 0.70676651 0.24655811
## nodefactor.deg.main.deg.pers.1.1 nodefactor.deg.main.deg.pers.1.2
## 0.13075392 0.21931096
## nodefactor.riskg.O2 nodefactor.riskg.O3
## NaN 0.08325448
## nodefactor.riskg.O4 nodefactor.riskg.Y1
## 0.82951859 NaN
## nodefactor.riskg.Y2 nodefactor.riskg.Y3
## 0.86264172 0.21019958
## nodefactor.riskg.Y4 nodefactor.race..wa.B
## 0.13482572 0.21067274
## nodefactor.race..wa.H nodefactor.region.EW
## 0.65164658 0.04007052
## nodefactor.region.OW nodematch.race..wa.B
## 0.79045401 NaN
## nodematch.race..wa.H nodematch.race..wa.O
## 0.94277115 0.30595257
## nodematch.region absdiff.sqrt.age
## 0.25813744 0.62137259
## Joint P-value (lower = worse): 0.1353675 .## Warning in formals(fun): argument is not a function
##
## MCMC diagnostics shown here are from the last round of simulation, prior to computation of final parameter estimates. Because the final estimates are refinements of those used for this simulation run, these diagnostics may understate model performance. To directly assess the performance of the final model on in-model statistics, please use the GOF command: gof(ergmFitObject, GOF=~model).Summary of model fit
Model 1
summary(est.i.buildup.unbal[[1]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55909a01c678>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.48697 0.04574 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 2
summary(est.i.buildup.unbal[[2]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x5590b53638c0>
##
## Iterations: 2 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -11.52227 0.05491 0 <1e-04 ***
## nodefactor.race..wa.B 0.27119 0.12122 0 0.0253 *
## nodefactor.race..wa.H -0.01052 0.10485 0 0.9201
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 3
summary(est.i.buildup.unbal[[3]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor("race..wa", base = 3) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x5590cb18de58>
##
## Iterations: 15 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 9.991 36671.497 100 1
## nodefactor.race..wa.B -20.966 36671.497 100 1
## nodefactor.race..wa.H -21.417 36671.497 100 1
## nodematch.race..wa.B -22.283 NA NA NA
## nodematch.race..wa.H 21.647 36671.497 100 1
## nodematch.race..wa.O -21.599 36671.497 100 1
## nodematch.role.class.I -Inf 0.000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 4
summary(est.i.buildup.unbal[[4]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodematch("race..wa", diff = TRUE) + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x5590e73c7b10>
##
## Iterations: 15 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 8.438e+01 4.162e+04 100 0.998
## nodefactor.deg.main.deg.pers.0.1 8.903e-01 9.087e-02 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.957e-01 1.703e-01 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.195e+00 1.733e-01 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 8.629e-01 9.885e-02 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 7.764e-01 9.574e-02 0 <1e-04 ***
## nodefactor.race..wa.B -9.525e+01 4.162e+04 100 0.998
## nodefactor.race..wa.H -9.564e+01 4.162e+04 100 0.998
## nodematch.race..wa.B 8.203e+01 NA NA NA
## nodematch.race..wa.H 9.589e+01 4.162e+04 100 0.998
## nodematch.race..wa.O -9.584e+01 4.162e+04 100 0.998
## nodematch.role.class.I -Inf 0.000e+00 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 5
summary(est.i.buildup.unbal[[5]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + offset(nodematch("role.class", diff = TRUE,
## keep = 1:2))
## <environment: 0x5591038b5650>
##
## Iterations: 14 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 101.56848 6757.78583 100 0.9880
## nodefactor.deg.main.deg.pers.0.1 0.88905 0.09102 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.70233 0.17183 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.22996 0.17173 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.81599 0.09922 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.75931 0.09639 0 <1e-04 ***
## nodefactor.race..wa.B -112.14700 6757.78583 100 0.9868
## nodefactor.race..wa.H -112.47556 6757.78583 100 0.9867
## nodefactor.region.EW -0.28984 0.11751 0 0.0136 *
## nodefactor.region.OW -0.43370 0.07578 0 <1e-04 ***
## nodematch.race..wa.B 98.34341 NA NA NA
## nodematch.race..wa.H 112.73996 6757.78584 100 0.9867
## nodematch.race..wa.O -112.69145 6757.78583 100 0.9867
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 6
summary(est.i.buildup.unbal[[6]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("race..wa",
## base = 3) + nodefactor("region", base = 2) + nodematch("race..wa",
## diff = TRUE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x55911fec3d80>
##
## Iterations: 15 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges 90.60542 NA NA NA
## nodefactor.deg.main.deg.pers.0.1 0.89118 0.09099 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -0.69817 0.17071 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.0 -2.22984 0.17398 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 0.82661 0.09956 0 <1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 0.75442 0.09676 0 <1e-04 ***
## nodefactor.race..wa.B -100.60096 NA NA NA
## nodefactor.race..wa.H -100.92762 NA NA NA
## nodefactor.region.EW -0.28696 0.11653 0 0.0138 *
## nodefactor.region.OW -0.43448 0.07618 0 <1e-04 ***
## nodematch.race..wa.B 83.09412 NA NA NA
## nodematch.race..wa.H 101.19042 NA NA NA
## nodematch.race..wa.O -101.14353 NA NA NA
## absdiff.sqrt.age -0.61070 0.06841 0 <1e-04 ***
## nodematch.role.class.I -Inf 0.00000 0 <1e-04 ***
## nodematch.role.class.R -Inf 0.00000 0 <1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 7
summary(est.i.buildup.unbal[[7]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") +
## nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) +
## nodematch("race..wa", diff = TRUE) + absdiff("sqrt.age") +
## offset(nodematch("role.class", diff = TRUE, keep = 1:2))
## <environment: 0x55913c6c49c8>
##
## Iterations: 31 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -2.278e+01 3.860e+04 100 0.999529
## nodefactor.deg.main.deg.pers.0.1 9.011e-01 9.121e-02 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.117e-01 1.701e-01 0 0.000324 ***
## nodefactor.deg.main.deg.pers.1.0 -2.173e+00 1.727e-01 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 9.129e-01 9.967e-02 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 6.626e-01 9.634e-02 0 < 1e-04 ***
## nodefactor.riskg.O2 8.895e-02 NA NA NA
## nodefactor.riskg.O3 1.668e+01 NA NA NA
## nodefactor.riskg.O4 1.965e+01 NA NA NA
## nodefactor.riskg.Y1 3.512e+00 NA NA NA
## nodefactor.riskg.Y2 1.550e+01 NA NA NA
## nodefactor.riskg.Y3 1.780e+01 NA NA NA
## nodefactor.riskg.Y4 2.019e+01 NA NA NA
## nodefactor.race..wa.B -2.501e+01 NA NA NA
## nodefactor.race..wa.H -2.527e+01 NA NA NA
## nodefactor.region.EW -1.512e-01 1.179e-01 0 0.199813
## nodefactor.region.OW -3.325e-01 7.602e-02 0 < 1e-04 ***
## nodematch.race..wa.B -2.158e+01 NA NA NA
## nodematch.race..wa.H 2.549e+01 NA NA NA
## nodematch.race..wa.O -2.556e+01 NA NA NA
## absdiff.sqrt.age -5.514e-01 7.195e-02 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfModel 8
summary(est.i.buildup.unbal[[8]])##
## ==========================
## Summary of model fit
## ==========================
##
## Formula: nw ~ edges + nodefactor(c("deg.main", "deg.pers")) + nodefactor("riskg") +
## nodefactor("race..wa", base = 3) + nodefactor("region", base = 2) +
## nodematch("race..wa", diff = TRUE) + nodematch("region",
## diff = FALSE) + absdiff("sqrt.age") + offset(nodematch("role.class",
## diff = TRUE, keep = 1:2))
## <environment: 0x559159232258>
##
## Iterations: 29 out of 400
##
## Monte Carlo MLE Results:
## Estimate Std. Error MCMC % p-value
## edges -2.752e+01 NA NA NA
## nodefactor.deg.main.deg.pers.0.1 9.008e-01 9.101e-02 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.0.2 -6.115e-01 1.710e-01 0 0.000349 ***
## nodefactor.deg.main.deg.pers.1.0 -2.173e+00 1.725e-01 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.1 9.130e-01 9.909e-02 0 < 1e-04 ***
## nodefactor.deg.main.deg.pers.1.2 6.629e-01 9.625e-02 0 < 1e-04 ***
## nodefactor.riskg.O2 -1.975e+00 NA NA NA
## nodefactor.riskg.O3 1.616e+01 2.255e+04 100 0.999428
## nodefactor.riskg.O4 1.913e+01 1.841e+04 100 0.999171
## nodefactor.riskg.Y1 2.439e+00 NA NA NA
## nodefactor.riskg.Y2 1.497e+01 2.377e+04 100 0.999497
## nodefactor.riskg.Y3 1.728e+01 1.681e+04 100 0.999180
## nodefactor.riskg.Y4 1.967e+01 1.989e+04 100 0.999211
## nodefactor.race..wa.B -2.074e+01 NA NA NA
## nodefactor.race..wa.H -2.099e+01 NA NA NA
## nodefactor.region.EW 5.909e-01 1.021e-01 0 < 1e-04 ***
## nodefactor.region.OW 4.858e-02 6.053e-02 0 0.422238
## nodematch.race..wa.B -1.766e+00 NA NA NA
## nodematch.race..wa.H 2.114e+01 NA NA NA
## nodematch.race..wa.O -2.128e+01 NA NA NA
## nodematch.region 1.787e+00 1.219e-01 0 < 1e-04 ***
## absdiff.sqrt.age -5.527e-01 7.182e-02 0 < 1e-04 ***
## nodematch.role.class.I -Inf 0.000e+00 0 < 1e-04 ***
## nodematch.role.class.R -Inf 0.000e+00 0 < 1e-04 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Log-likelihood was not estimated for this fit.
## To get deviances, AIC, and/or BIC from fit `object$fit` run
## > object$fit<-logLik(object$fit, add=TRUE)
## to add it to the object or rerun this function with eval.loglik=TRUE.
##
## The following terms are fixed by offset and are not estimated:
## nodematch.role.class.I nodematch.role.class.R
##
##
## Dissolution Coefficients
## =======================
## Dissolution Model: ~offset(edges)
## Target Statistics: 1
## Crude Coefficient: -Inf
## Mortality/Exit Rate: 0
## Adjusted Coefficient: -InfNetwork diagnostics
Model 1
(dx_inst1 <- netdx(est.i.buildup.unbal[[1]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 478.151 -0.001 21.588
## nodefactor.deg.main.deg.pers.0.1 NA 66.628 NA 8.428
## nodefactor.deg.main.deg.pers.0.2 NA 69.894 NA 8.629
## nodefactor.deg.main.deg.pers.1.0 NA 310.384 NA 20.104
## nodefactor.deg.main.deg.pers.1.1 NA 54.334 NA 7.526
## nodefactor.deg.main.deg.pers.1.2 NA 63.214 NA 8.140
## nodefactor.riskg.O2 NA 55.267 NA 7.636
## nodefactor.riskg.O3 NA 54.681 NA 7.615
## nodefactor.riskg.O4 NA 54.918 NA 7.607
## nodefactor.riskg.Y1 NA 183.458 NA 14.666
## nodefactor.riskg.Y2 NA 184.473 NA 14.793
## nodefactor.riskg.Y3 NA 184.753 NA 14.880
## nodefactor.riskg.Y4 NA 184.013 NA 14.713
## nodefactor.race..wa.B NA 58.279 NA 7.874
## nodefactor.race..wa.H NA 103.653 NA 10.693
## nodefactor.region.EW NA 96.562 NA 10.377
## nodefactor.region.OW NA 313.897 NA 20.258
## nodematch.race..wa.B NA 1.777 NA 1.314
## nodematch.race..wa.H NA 5.607 NA 2.370
## nodematch.race..wa.O NA 329.927 NA 17.978
## nodematch.region NA 212.223 NA 14.458
## absdiff.sqrt.age NA 544.622 NA 30.164
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst1, type="formation")
plot(dx_inst1, type="duration")
plot(dx_inst1, type="dissolution")
Model 2
(dx_inst2 <- netdx(est.i.buildup.unbal[[2]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 478.600 0.000 21.803
## nodefactor.deg.main.deg.pers.0.1 NA 67.093 NA 8.477
## nodefactor.deg.main.deg.pers.0.2 NA 70.228 NA 8.640
## nodefactor.deg.main.deg.pers.1.0 NA 309.292 NA 20.177
## nodefactor.deg.main.deg.pers.1.1 NA 54.026 NA 7.481
## nodefactor.deg.main.deg.pers.1.2 NA 63.229 NA 8.268
## nodefactor.riskg.O2 NA 55.433 NA 7.675
## nodefactor.riskg.O3 NA 54.887 NA 7.532
## nodefactor.riskg.O4 NA 54.832 NA 7.600
## nodefactor.riskg.Y1 NA 183.662 NA 14.836
## nodefactor.riskg.Y2 NA 184.776 NA 14.915
## nodefactor.riskg.Y3 NA 184.370 NA 14.859
## nodefactor.riskg.Y4 NA 184.497 NA 14.793
## nodefactor.race..wa.B 75.186 75.077 -0.001 8.988
## nodefactor.race..wa.H 100.835 100.951 0.001 10.615
## nodefactor.region.EW NA 95.490 NA 10.341
## nodefactor.region.OW NA 313.575 NA 20.398
## nodematch.race..wa.B NA 2.929 NA 1.709
## nodematch.race..wa.H NA 5.316 NA 2.306
## nodematch.race..wa.O NA 318.696 NA 17.679
## nodematch.region NA 212.870 NA 14.501
## absdiff.sqrt.age NA 545.496 NA 30.208
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst2, type="formation")
plot(dx_inst2, type="duration")
plot(dx_inst2, type="dissolution")
Model 3
(dx_inst3 <- netdx(est.i.buildup.unbal[[3]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 472.171 -0.013 22.093
## nodefactor.deg.main.deg.pers.0.1 NA 66.144 NA 8.421
## nodefactor.deg.main.deg.pers.0.2 NA 69.451 NA 8.658
## nodefactor.deg.main.deg.pers.1.0 NA 305.117 NA 20.556
## nodefactor.deg.main.deg.pers.1.1 NA 53.075 NA 7.424
## nodefactor.deg.main.deg.pers.1.2 NA 62.183 NA 8.121
## nodefactor.riskg.O2 NA 54.728 NA 7.632
## nodefactor.riskg.O3 NA 54.288 NA 7.578
## nodefactor.riskg.O4 NA 54.204 NA 7.687
## nodefactor.riskg.Y1 NA 180.928 NA 14.849
## nodefactor.riskg.Y2 NA 182.341 NA 14.800
## nodefactor.riskg.Y3 NA 181.632 NA 14.656
## nodefactor.riskg.Y4 NA 182.240 NA 14.769
## nodefactor.race..wa.B 75.186 80.698 0.073 9.033
## nodefactor.race..wa.H 100.835 106.476 0.056 11.171
## nodefactor.region.EW NA 94.642 NA 10.229
## nodefactor.region.OW NA 308.361 NA 20.443
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 7.484 -0.436 2.754
## nodematch.race..wa.O 286.884 292.482 0.020 17.165
## nodematch.region NA 210.233 NA 14.609
## absdiff.sqrt.age NA 538.550 NA 30.700
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst3, type="formation")
plot(dx_inst3, type="duration")
plot(dx_inst3, type="dissolution")
Model 4
(dx_inst4 <- netdx(est.i.buildup.unbal[[4]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 457.358 -0.044 22.510
## nodefactor.deg.main.deg.pers.0.1 173.175 161.054 -0.070 14.391
## nodefactor.deg.main.deg.pers.0.2 37.217 37.117 -0.003 6.259
## nodefactor.deg.main.deg.pers.1.0 36.568 36.591 0.001 6.064
## nodefactor.deg.main.deg.pers.1.1 135.364 126.785 -0.063 12.634
## nodefactor.deg.main.deg.pers.1.2 145.870 138.280 -0.052 12.989
## nodefactor.riskg.O2 NA 51.199 NA 7.405
## nodefactor.riskg.O3 NA 52.176 NA 7.526
## nodefactor.riskg.O4 NA 53.042 NA 7.566
## nodefactor.riskg.Y1 NA 170.601 NA 14.429
## nodefactor.riskg.Y2 NA 174.565 NA 14.776
## nodefactor.riskg.Y3 NA 178.795 NA 14.709
## nodefactor.riskg.Y4 NA 179.387 NA 14.726
## nodefactor.race..wa.B 75.186 75.418 0.003 8.972
## nodefactor.race..wa.H 100.835 102.511 0.017 10.970
## nodefactor.region.EW NA 91.995 NA 10.231
## nodefactor.region.OW NA 309.464 NA 20.847
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 7.025 -0.471 2.651
## nodematch.race..wa.O 286.884 286.454 -0.001 17.233
## nodematch.region NA 200.974 NA 14.573
## absdiff.sqrt.age NA 522.794 NA 31.006
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst4, type="formation")
plot(dx_inst4, type="duration")
plot(dx_inst4, type="dissolution")
Model 5
(dx_inst5 <- netdx(est.i.buildup.unbal[[5]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 454.340 -0.051 22.602
## nodefactor.deg.main.deg.pers.0.1 173.175 159.186 -0.081 14.583
## nodefactor.deg.main.deg.pers.0.2 37.217 37.268 0.001 6.165
## nodefactor.deg.main.deg.pers.1.0 36.568 36.634 0.002 6.115
## nodefactor.deg.main.deg.pers.1.1 135.364 125.559 -0.072 12.475
## nodefactor.deg.main.deg.pers.1.2 145.870 136.589 -0.064 13.170
## nodefactor.riskg.O2 NA 50.986 NA 7.357
## nodefactor.riskg.O3 NA 51.654 NA 7.376
## nodefactor.riskg.O4 NA 53.332 NA 7.533
## nodefactor.riskg.Y1 NA 167.251 NA 14.365
## nodefactor.riskg.Y2 NA 173.222 NA 14.631
## nodefactor.riskg.Y3 NA 178.388 NA 14.994
## nodefactor.riskg.Y4 NA 179.833 NA 14.826
## nodefactor.race..wa.B 75.186 74.682 -0.007 8.862
## nodefactor.race..wa.H 100.835 101.613 0.008 10.964
## nodefactor.region.EW 83.389 81.191 -0.026 9.453
## nodefactor.region.OW 242.159 237.964 -0.017 17.266
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 6.960 -0.476 2.626
## nodematch.race..wa.O 286.884 285.004 -0.007 17.318
## nodematch.region NA 225.097 NA 15.815
## absdiff.sqrt.age NA 519.067 NA 31.054
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst5, type="formation")
plot(dx_inst5, type="duration")
plot(dx_inst5, type="dissolution")
Model 6
(dx_inst6 <- netdx(est.i.buildup.unbal[[6]], nsims = 10, nsteps = 1000, ncores = 4, nwstats.formula = est.i.buildup.unbal[[8]]$formation))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 446.085 -0.068 22.951
## nodefactor.deg.main.deg.pers.0.1 173.175 154.292 -0.109 14.452
## nodefactor.deg.main.deg.pers.0.2 37.217 37.210 0.000 6.218
## nodefactor.deg.main.deg.pers.1.0 36.568 36.481 -0.002 6.163
## nodefactor.deg.main.deg.pers.1.1 135.364 121.952 -0.099 12.463
## nodefactor.deg.main.deg.pers.1.2 145.870 132.945 -0.089 13.183
## nodefactor.riskg.O2 NA 46.306 NA 7.204
## nodefactor.riskg.O3 NA 47.113 NA 7.235
## nodefactor.riskg.O4 NA 48.703 NA 7.450
## nodefactor.riskg.Y1 NA 167.969 NA 14.447
## nodefactor.riskg.Y2 NA 173.826 NA 14.982
## nodefactor.riskg.Y3 NA 178.506 NA 14.976
## nodefactor.riskg.Y4 NA 180.414 NA 15.312
## nodefactor.race..wa.B 75.186 72.377 -0.037 8.903
## nodefactor.race..wa.H 100.835 99.909 -0.009 10.949
## nodefactor.region.EW 83.389 80.133 -0.039 9.417
## nodefactor.region.OW 242.159 235.312 -0.028 17.628
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 6.843 -0.484 2.631
## nodematch.race..wa.O 286.884 280.643 -0.022 17.333
## nodematch.region NA 220.165 NA 15.824
## absdiff.sqrt.age 379.987 367.744 -0.032 22.836
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst6, type="formation")
plot(dx_inst6, type="duration")
plot(dx_inst6, type="dissolution")
Model 7
(dx_inst7 <- netdx(est.i.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 245.389 -0.487 33.031
## nodefactor.deg.main.deg.pers.0.1 173.175 67.775 -0.609 13.006
## nodefactor.deg.main.deg.pers.0.2 37.217 27.004 -0.274 5.924
## nodefactor.deg.main.deg.pers.1.0 36.568 35.360 -0.033 6.133
## nodefactor.deg.main.deg.pers.1.1 135.364 53.190 -0.607 10.671
## nodefactor.deg.main.deg.pers.1.2 145.870 63.025 -0.568 12.096
## nodefactor.riskg.O2 0.401 0.000 -1.000 0.000
## nodefactor.riskg.O3 6.856 5.344 -0.221 2.330
## nodefactor.riskg.O4 109.513 67.684 -0.382 11.784
## nodefactor.riskg.Y1 1.349 0.000 -1.000 0.000
## nodefactor.riskg.Y2 8.202 6.746 -0.178 2.621
## nodefactor.riskg.Y3 70.786 64.750 -0.085 8.605
## nodefactor.riskg.Y4 762.012 346.255 -0.546 54.176
## nodefactor.race..wa.B 75.186 35.660 -0.526 7.612
## nodefactor.race..wa.H 100.835 52.334 -0.481 9.910
## nodefactor.region.EW 83.389 45.145 -0.459 8.778
## nodefactor.region.OW 242.159 139.210 -0.425 20.481
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 3.174 -0.761 1.837
## nodematch.race..wa.O 286.884 160.569 -0.440 22.279
## absdiff.sqrt.age 379.987 222.382 -0.415 29.943
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst7, type="formation")
plot(dx_inst7, type="duration")
plot(dx_inst7, type="dissolution")
Model 8
(dx_inst8 <- netdx(est.i.buildup.unbal[[7]], nsims = 10, nsteps = 1000, ncores = 4))##
## Network Diagnostics
## -----------------------
## - Simulating 10 networks
## - Calculating formation statistics
## - Calculating duration statistics
## - Calculating dissolution statistics
## ## EpiModel Network Diagnostics
## =======================
## Diagnostic Method: Dynamic
## Simulations: 10
## Time Steps per Sim: 1000
##
## Formation Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## edges 478.512 245.723 -0.486 32.751
## nodefactor.deg.main.deg.pers.0.1 173.175 67.699 -0.609 12.910
## nodefactor.deg.main.deg.pers.0.2 37.217 27.002 -0.274 5.907
## nodefactor.deg.main.deg.pers.1.0 36.568 35.325 -0.034 6.057
## nodefactor.deg.main.deg.pers.1.1 135.364 53.148 -0.607 10.490
## nodefactor.deg.main.deg.pers.1.2 145.870 63.154 -0.567 12.022
## nodefactor.riskg.O2 0.401 0.000 -1.000 0.000
## nodefactor.riskg.O3 6.856 5.368 -0.217 2.355
## nodefactor.riskg.O4 109.513 67.841 -0.381 11.793
## nodefactor.riskg.Y1 1.349 0.000 -1.000 0.000
## nodefactor.riskg.Y2 8.202 6.817 -0.169 2.616
## nodefactor.riskg.Y3 70.786 64.652 -0.087 8.615
## nodefactor.riskg.Y4 762.012 346.767 -0.545 53.571
## nodefactor.race..wa.B 75.186 35.785 -0.524 7.613
## nodefactor.race..wa.H 100.835 52.678 -0.478 9.934
## nodefactor.region.EW 83.389 45.352 -0.456 8.683
## nodefactor.region.OW 242.159 139.693 -0.423 20.327
## nodematch.race..wa.B 2.538 0.000 -1.000 0.000
## nodematch.race..wa.H 13.275 3.159 -0.762 1.833
## nodematch.race..wa.O 286.884 160.420 -0.441 22.136
## absdiff.sqrt.age 379.987 222.718 -0.414 29.684
## nodematch.role.class.I NA 0.000 NA 0.000
## nodematch.role.class.R NA 0.000 NA 0.000
##
## Dissolution Diagnostics
## -----------------------
## Target Sim Mean Pct Diff Sim SD
## Edge Duration 1 1 0 0
## Pct Edges Diss 1 1 0 0plot(dx_inst8, type="formation")
plot(dx_inst8, type="duration")
plot(dx_inst8, type="dissolution")